From 103db9e135e9433dec65f0254871c1d4e681d88c Mon Sep 17 00:00:00 2001
From: AMIT CHEJARA <amit@AMIT-MacBook-Air.local>
Date: Thu, 11 Dec 2025 17:47:22 +0530
Subject: [PATCH 1/4] Create a new data module instance across all optimization
 trials

---
 .../torch_lightning_experiment.py             | 49 ++++++++++---------
 1 file changed, 26 insertions(+), 23 deletions(-)

diff --git a/src/hyperactive/experiment/integrations/torch_lightning_experiment.py b/src/hyperactive/experiment/integrations/torch_lightning_experiment.py
index 0bdd1f80..886de18c 100644
--- a/src/hyperactive/experiment/integrations/torch_lightning_experiment.py
+++ b/src/hyperactive/experiment/integrations/torch_lightning_experiment.py
@@ -21,8 +21,10 @@ class TorchExperiment(BaseExperiment):
 
     Parameters
     ----------
-    datamodule : L.LightningDataModule
-        A PyTorch Lightning DataModule that handles data loading and preparation.
+    data_module : type
+        A PyTorch Lightning DataModule class (not an instance) that
+        handles data loading and preparation. It will be instantiated
+        with hyperparameters during optimization.
     lightning_module : type
         A PyTorch Lightning Module class (not an instance) that will be instantiated
         with hyperparameters during optimization.
@@ -93,20 +95,18 @@ class TorchExperiment(BaseExperiment):
     ...     def val_dataloader(self):
     ...         return DataLoader(self.val, batch_size=self.batch_size)
     >>>
-    >>> datamodule = RandomDataModule(batch_size=16)
-    >>> datamodule.setup()
-    >>>
     >>> # Create Experiment
     >>> experiment = TorchExperiment(
-    ...     datamodule=datamodule,
+    ...     data_module=RandomDataModule,
     ...     lightning_module=SimpleLightningModule,
     ...     trainer_kwargs={'max_epochs': 3},
     ...     objective_metric="val_loss"
     ... )
     >>>
-    >>> params = {"input_dim": 10, "hidden_dim": 16, "lr": 1e-3}
+    >>> lm_params = {"input_dim": 10, "hidden_dim": 16, "lr": 1e-3}
+    >>> dm_params = {"batch_size": 32}
     >>>
-    >>> val_result, metadata = experiment._evaluate(params)
+    >>> val_result, metadata = experiment._evaluate(lm_params, dm_params)
     """
 
     _tags = {
@@ -118,12 +118,12 @@ class TorchExperiment(BaseExperiment):
 
     def __init__(
         self,
-        datamodule,
+        data_module,
         lightning_module,
         trainer_kwargs=None,
         objective_metric: str = "val_loss",
     ):
-        self.datamodule = datamodule
+        self.data_module = data_module
         self.lightning_module = lightning_module
         self.trainer_kwargs = trainer_kwargs or {}
         self.objective_metric = objective_metric
@@ -154,7 +154,7 @@ def _paramnames(self):
         sig = inspect.signature(self.lightning_module.__init__)
         return [p for p in sig.parameters.keys() if p != "self"]
 
-    def _evaluate(self, params):
+    def _evaluate(self, lm_params, dm_params):
         """Evaluate the parameters.
 
         Parameters
@@ -172,9 +172,10 @@ def _evaluate(self, params):
         import lightning as L
 
         try:
-            model = self.lightning_module(**params)
+            model = self.lightning_module(**lm_params)
             trainer = L.Trainer(**self._trainer_kwargs)
-            trainer.fit(model, self.datamodule)
+            data = self.data_module(**dm_params)
+            trainer.fit(model, data)
 
             val_result = trainer.callback_metrics.get(self.objective_metric)
             metadata = {}
@@ -195,7 +196,7 @@ def _evaluate(self, params):
             return val_result, metadata
 
         except Exception as e:
-            print(f"Training failed with params {params}: {e}")
+            print(f"Training failed with params {lm_params}: {e}")
             return np.float64(float("inf")), {}
 
     @classmethod
@@ -265,10 +266,8 @@ def train_dataloader(self):
             def val_dataloader(self):
                 return DataLoader(self.val, batch_size=self.batch_size)
 
-        datamodule = RandomDataModule(batch_size=16)
-
         params = {
-            "datamodule": datamodule,
+            "data_module": RandomDataModule,
             "lightning_module": SimpleLightningModule,
             "trainer_kwargs": {
                 "max_epochs": 1,
@@ -339,10 +338,8 @@ def train_dataloader(self):
             def val_dataloader(self):
                 return DataLoader(self.val, batch_size=self.batch_size)
 
-        datamodule2 = RegressionDataModule(batch_size=16, num_samples=150)
-
         params2 = {
-            "datamodule": datamodule2,
+            "data_module": RegressionDataModule,
             "lightning_module": RegressionModule,
             "trainer_kwargs": {
                 "max_epochs": 1,
@@ -368,6 +365,12 @@ def _get_score_params(cls):
         list of dict
             The parameters to be used for scoring.
         """
-        score_params1 = {"input_dim": 10, "hidden_dim": 20, "lr": 0.001}
-        score_params2 = {"num_layers": 3, "hidden_size": 64, "dropout": 0.2}
-        return [score_params1, score_params2]
+        score_lm_params1 = {"input_dim": 10, "hidden_dim": 20, "lr": 0.001}
+        score_lm_params2 = {"num_layers": 3, "hidden_size": 64, "dropout": 0.2}
+        score_dm_params1 = {"batch_size": 16}
+        score_dm_params2 = {"batch_size": 8, "num_samples": 200}
+
+        return [
+            {"lm_params": score_lm_params1, "dm_params": score_dm_params1},
+            {"lm_params": score_lm_params2, "dm_params": score_dm_params2},
+        ]

From 1d9a535201d7d0b624886e53af37412226d74d66 Mon Sep 17 00:00:00 2001
From: AMIT CHEJARA <amit@AMIT-MacBook-Air.local>
Date: Thu, 11 Dec 2025 18:31:34 +0530
Subject: [PATCH 2/4] Show both lm_params and dm_params for a failed training
 run

---
 .../experiment/integrations/torch_lightning_experiment.py     | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/hyperactive/experiment/integrations/torch_lightning_experiment.py b/src/hyperactive/experiment/integrations/torch_lightning_experiment.py
index 886de18c..fca35e46 100644
--- a/src/hyperactive/experiment/integrations/torch_lightning_experiment.py
+++ b/src/hyperactive/experiment/integrations/torch_lightning_experiment.py
@@ -196,7 +196,9 @@ def _evaluate(self, lm_params, dm_params):
             return val_result, metadata
 
         except Exception as e:
-            print(f"Training failed with params {lm_params}: {e}")
+            print(
+                f"Training failed (lm_params={lm_params}, dm_params={dm_params}): {e}"
+            )
             return np.float64(float("inf")), {}
 
     @classmethod

From 1cdc133a20e9a6bef8c9c0b9278bf82df45f6da2 Mon Sep 17 00:00:00 2001
From: AMIT CHEJARA <amit@AMIT-MacBook-Air.local>
Date: Thu, 11 Dec 2025 20:45:50 +0530
Subject: [PATCH 3/4] Accept dm_kwargs to create a new instance of the data
 module for each optimization trial

---
 .../torch_lightning_experiment.py             | 35 +++++++++----------
 1 file changed, 17 insertions(+), 18 deletions(-)

diff --git a/src/hyperactive/experiment/integrations/torch_lightning_experiment.py b/src/hyperactive/experiment/integrations/torch_lightning_experiment.py
index fca35e46..f0407c24 100644
--- a/src/hyperactive/experiment/integrations/torch_lightning_experiment.py
+++ b/src/hyperactive/experiment/integrations/torch_lightning_experiment.py
@@ -30,6 +30,8 @@ class TorchExperiment(BaseExperiment):
         with hyperparameters during optimization.
     trainer_kwargs : dict, optional (default=None)
         A dictionary of keyword arguments to pass to the PyTorch Lightning Trainer.
+    dm_kwargs : dict, optional (default=None)
+        A dictionary of keyword arguments to pass to the Data Module upon instantiation.
     objective_metric : str, optional (default='val_loss')
         The metric used to evaluate the model's performance. This should correspond
         to a metric logged in the LightningModule during validation.
@@ -100,13 +102,13 @@ class TorchExperiment(BaseExperiment):
     ...     data_module=RandomDataModule,
     ...     lightning_module=SimpleLightningModule,
     ...     trainer_kwargs={'max_epochs': 3},
+    ...     dm_kwargs={'batch_size': 16},
     ...     objective_metric="val_loss"
     ... )
     >>>
-    >>> lm_params = {"input_dim": 10, "hidden_dim": 16, "lr": 1e-3}
-    >>> dm_params = {"batch_size": 32}
+    >>> params = {"input_dim": 10, "hidden_dim": 16, "lr": 1e-3}
     >>>
-    >>> val_result, metadata = experiment._evaluate(lm_params, dm_params)
+    >>> val_result, metadata = experiment._evaluate(params)
     """
 
     _tags = {
@@ -121,11 +123,13 @@ def __init__(
         data_module,
         lightning_module,
         trainer_kwargs=None,
+        dm_kwargs=None,
         objective_metric: str = "val_loss",
     ):
         self.data_module = data_module
         self.lightning_module = lightning_module
         self.trainer_kwargs = trainer_kwargs or {}
+        self.dm_kwargs = dm_kwargs or {}
         self.objective_metric = objective_metric
 
         super().__init__()
@@ -154,7 +158,7 @@ def _paramnames(self):
         sig = inspect.signature(self.lightning_module.__init__)
         return [p for p in sig.parameters.keys() if p != "self"]
 
-    def _evaluate(self, lm_params, dm_params):
+    def _evaluate(self, params):
         """Evaluate the parameters.
 
         Parameters
@@ -172,9 +176,9 @@ def _evaluate(self, lm_params, dm_params):
         import lightning as L
 
         try:
-            model = self.lightning_module(**lm_params)
+            model = self.lightning_module(**params)
             trainer = L.Trainer(**self._trainer_kwargs)
-            data = self.data_module(**dm_params)
+            data = self.data_module(**self.dm_kwargs)
             trainer.fit(model, data)
 
             val_result = trainer.callback_metrics.get(self.objective_metric)
@@ -196,9 +200,7 @@ def _evaluate(self, lm_params, dm_params):
             return val_result, metadata
 
         except Exception as e:
-            print(
-                f"Training failed (lm_params={lm_params}, dm_params={dm_params}): {e}"
-            )
+            print(f"Training failed with params {params}: {e}")
             return np.float64(float("inf")), {}
 
     @classmethod
@@ -277,6 +279,7 @@ def val_dataloader(self):
                 "enable_model_summary": False,
                 "logger": False,
             },
+            "dm_kwargs": {"batch_size": 16},
             "objective_metric": "val_loss",
         }
 
@@ -349,6 +352,7 @@ def val_dataloader(self):
                 "enable_model_summary": False,
                 "logger": False,
             },
+            "dm_kwargs": {"batch_size": 8, "num_samples": 200},
             "objective_metric": "val_loss",
         }
 
@@ -367,12 +371,7 @@ def _get_score_params(cls):
         list of dict
             The parameters to be used for scoring.
         """
-        score_lm_params1 = {"input_dim": 10, "hidden_dim": 20, "lr": 0.001}
-        score_lm_params2 = {"num_layers": 3, "hidden_size": 64, "dropout": 0.2}
-        score_dm_params1 = {"batch_size": 16}
-        score_dm_params2 = {"batch_size": 8, "num_samples": 200}
-
-        return [
-            {"lm_params": score_lm_params1, "dm_params": score_dm_params1},
-            {"lm_params": score_lm_params2, "dm_params": score_dm_params2},
-        ]
+        score_params1 = {"input_dim": 10, "hidden_dim": 20, "lr": 0.001}
+        score_params2 = {"num_layers": 3, "hidden_size": 64, "dropout": 0.2}
+
+        return [score_params1, score_params2]

From fedece5bf9a8c234d56b8b068121770e9c9a0b6a Mon Sep 17 00:00:00 2001
From: AMIT CHEJARA <amit@AMIT-MacBook-Air.local>
Date: Thu, 11 Dec 2025 22:47:56 +0530
Subject: [PATCH 4/4] Resolve merge conflicts

---
 .github/PULL_REQUEST_TEMPLATE.md              |   37 +
 .github/auto-label-config.yml                 |   41 +
 .github/draft-changelog-config.yml            |   67 +
 .github/workflows/auto-label-prs.yml          |   35 +
 .github/workflows/docs.yml                    |   90 ++
 .github/workflows/draft-changelog.yml         |   39 +
 .github/workflows/test.yml                    |  350 ++++-
 .gitignore                                    |    3 +-
 .readthedocs.yml                              |   19 +
 Makefile                                      |   10 +-
 README.md                                     |    2 +-
 docs/README.md                                |   92 ++
 docs/requirements.txt                         |   19 +
 docs/source/Makefile                          |   31 +
 docs/source/_snippets/__init__.py             |   12 +
 docs/source/_snippets/conftest.py             |   46 +
 docs/source/_snippets/examples/__init__.py    |    1 +
 .../_snippets/examples/advanced_examples.py   |   77 +
 .../_snippets/examples/basic_examples.py      |   73 +
 .../_snippets/getting_started/__init__.py     |    1 +
 .../getting_started/bayesian_optimizer.py     |   41 +
 .../getting_started/index_bayesian.py         |   37 +
 .../getting_started/index_custom_function.py  |   39 +
 .../getting_started/index_sklearn_tuning.py   |   35 +
 .../_snippets/getting_started/quick_start.py  |   41 +
 .../getting_started/sklearn_optcv.py          |   41 +
 .../getting_started/sklearn_random_forest.py  |   48 +
 .../source/_snippets/installation/__init__.py |    1 +
 .../installation/verify_installation.py       |   32 +
 docs/source/_snippets/user_guide/__init__.py  |    1 +
 .../_snippets/user_guide/experiments.py       |  231 +++
 .../_snippets/user_guide/integrations.py      |  277 ++++
 .../_snippets/user_guide/introduction.py      |  124 ++
 .../source/_snippets/user_guide/optimizers.py |  324 ++++
 docs/source/_static/css/custom.css            | 1300 +++++++++++++++++
 docs/source/_static/fields.css                |   21 +
 .../_static/images/badges/generate_badges.py  |  188 +++
 docs/source/_static/images/badges/license.svg |   11 +
 docs/source/_static/images/badges/python.svg  |   11 +
 docs/source/_static/images/badges/sponsor.svg |   11 +
 docs/source/_static/images/badges/version.svg |   11 +
 docs/source/_static/images/bayes_convex.gif   |  Bin 0 -> 183902 bytes
 docs/source/_static/images/logo.png           |  Bin 0 -> 183993 bytes
 docs/source/_static/images/navbar_logo.png    |  Bin 0 -> 442647 bytes
 docs/source/_static/images/navbar_logo.svg    |  326 +++++
 .../_static/images/navbar_logo_dark.svg       |  326 +++++
 docs/source/_templates/class.rst              |   10 +
 docs/source/_templates/function.rst           |   10 +
 docs/source/_templates/layout.html            |    6 +
 docs/source/about.rst                         |  107 ++
 docs/source/about/history.rst                 |  126 ++
 docs/source/about/license.rst                 |   99 ++
 docs/source/about/team.rst                    |  102 ++
 docs/source/api_reference.rst                 |   21 +
 docs/source/api_reference/base.rst            |   17 +
 .../api_reference/experiments_benchmarks.rst  |   19 +
 .../api_reference/experiments_function.rst    |   20 +
 .../experiments_integrations.rst              |   57 +
 docs/source/api_reference/optimizers.rst      |  128 ++
 .../api_reference/sklearn_integration.rst     |   18 +
 docs/source/api_reference/utilities.rst       |   18 +
 docs/source/conf.py                           |  435 ++++++
 docs/source/examples.rst                      |   83 ++
 docs/source/examples/general.rst              |   47 +
 docs/source/examples/global_search.rst        |   44 +
 docs/source/examples/integrations.rst         |   49 +
 docs/source/examples/interactive_tutorial.rst |   50 +
 docs/source/examples/local_search.rst         |   41 +
 docs/source/examples/optuna_backend.rst       |   56 +
 docs/source/examples/other.rst                |   46 +
 docs/source/examples/population_based.rst     |   45 +
 .../examples/sequential_model_based.rst       |   43 +
 docs/source/examples/sklearn_backend.rst      |   57 +
 docs/source/faq.rst                           |   40 +
 docs/source/faq/advanced_usage.rst            |   69 +
 docs/source/faq/common_issues.rst             |   72 +
 docs/source/faq/getting_help.rst              |   28 +
 docs/source/faq/getting_started.rst           |   53 +
 docs/source/faq/integrations.rst              |   64 +
 docs/source/faq/search_space.rst              |   49 +
 docs/source/get_involved.rst                  |  123 ++
 docs/source/get_involved/code_of_conduct.rst  |  114 ++
 docs/source/get_involved/contributing.rst     |  250 ++++
 docs/source/get_started.rst                   |  111 ++
 docs/source/index.rst                         |  498 +++++++
 docs/source/installation.rst                  |  205 +++
 docs/source/troubleshooting.rst               |   39 +
 docs/source/troubleshooting/experiments.rst   |   53 +
 docs/source/troubleshooting/getting_help.rst  |   20 +
 docs/source/troubleshooting/installation.rst  |   61 +
 docs/source/troubleshooting/performance.rst   |   69 +
 docs/source/troubleshooting/results.rst       |   78 +
 .../source/troubleshooting/runtime_errors.rst |   60 +
 docs/source/user_guide.rst                    |   99 ++
 docs/source/user_guide/experiments.rst        |  137 ++
 docs/source/user_guide/integrations.rst       |  147 ++
 docs/source/user_guide/introduction.rst       |  179 +++
 docs/source/user_guide/migration.rst          |  361 +++++
 docs/source/user_guide/optimizers.rst         |  325 +++++
 docs/source/user_guide/search_spaces.rst      |  355 +++++
 docs/tests/__init__.py                        |    1 +
 docs/tests/test_doc_snippets.py               |  270 ++++
 pyproject.toml                                |   12 +-
 requirements/requirements-test.in             |    2 +-
 .../integrations/sklearn/opt_cv.py            |    3 +-
 .../opt/optuna/_cmaes_optimizer.py            |    5 +-
 106 files changed, 10194 insertions(+), 34 deletions(-)
 create mode 100644 .github/PULL_REQUEST_TEMPLATE.md
 create mode 100644 .github/auto-label-config.yml
 create mode 100644 .github/draft-changelog-config.yml
 create mode 100644 .github/workflows/auto-label-prs.yml
 create mode 100644 .github/workflows/docs.yml
 create mode 100644 .github/workflows/draft-changelog.yml
 create mode 100644 .readthedocs.yml
 create mode 100644 docs/README.md
 create mode 100644 docs/requirements.txt
 create mode 100644 docs/source/Makefile
 create mode 100644 docs/source/_snippets/__init__.py
 create mode 100644 docs/source/_snippets/conftest.py
 create mode 100644 docs/source/_snippets/examples/__init__.py
 create mode 100644 docs/source/_snippets/examples/advanced_examples.py
 create mode 100644 docs/source/_snippets/examples/basic_examples.py
 create mode 100644 docs/source/_snippets/getting_started/__init__.py
 create mode 100644 docs/source/_snippets/getting_started/bayesian_optimizer.py
 create mode 100644 docs/source/_snippets/getting_started/index_bayesian.py
 create mode 100644 docs/source/_snippets/getting_started/index_custom_function.py
 create mode 100644 docs/source/_snippets/getting_started/index_sklearn_tuning.py
 create mode 100644 docs/source/_snippets/getting_started/quick_start.py
 create mode 100644 docs/source/_snippets/getting_started/sklearn_optcv.py
 create mode 100644 docs/source/_snippets/getting_started/sklearn_random_forest.py
 create mode 100644 docs/source/_snippets/installation/__init__.py
 create mode 100644 docs/source/_snippets/installation/verify_installation.py
 create mode 100644 docs/source/_snippets/user_guide/__init__.py
 create mode 100644 docs/source/_snippets/user_guide/experiments.py
 create mode 100644 docs/source/_snippets/user_guide/integrations.py
 create mode 100644 docs/source/_snippets/user_guide/introduction.py
 create mode 100644 docs/source/_snippets/user_guide/optimizers.py
 create mode 100644 docs/source/_static/css/custom.css
 create mode 100644 docs/source/_static/fields.css
 create mode 100644 docs/source/_static/images/badges/generate_badges.py
 create mode 100644 docs/source/_static/images/badges/license.svg
 create mode 100644 docs/source/_static/images/badges/python.svg
 create mode 100644 docs/source/_static/images/badges/sponsor.svg
 create mode 100644 docs/source/_static/images/badges/version.svg
 create mode 100644 docs/source/_static/images/bayes_convex.gif
 create mode 100644 docs/source/_static/images/logo.png
 create mode 100644 docs/source/_static/images/navbar_logo.png
 create mode 100644 docs/source/_static/images/navbar_logo.svg
 create mode 100644 docs/source/_static/images/navbar_logo_dark.svg
 create mode 100644 docs/source/_templates/class.rst
 create mode 100644 docs/source/_templates/function.rst
 create mode 100644 docs/source/_templates/layout.html
 create mode 100644 docs/source/about.rst
 create mode 100644 docs/source/about/history.rst
 create mode 100644 docs/source/about/license.rst
 create mode 100644 docs/source/about/team.rst
 create mode 100644 docs/source/api_reference.rst
 create mode 100644 docs/source/api_reference/base.rst
 create mode 100644 docs/source/api_reference/experiments_benchmarks.rst
 create mode 100644 docs/source/api_reference/experiments_function.rst
 create mode 100644 docs/source/api_reference/experiments_integrations.rst
 create mode 100644 docs/source/api_reference/optimizers.rst
 create mode 100644 docs/source/api_reference/sklearn_integration.rst
 create mode 100644 docs/source/api_reference/utilities.rst
 create mode 100644 docs/source/conf.py
 create mode 100644 docs/source/examples.rst
 create mode 100644 docs/source/examples/general.rst
 create mode 100644 docs/source/examples/global_search.rst
 create mode 100644 docs/source/examples/integrations.rst
 create mode 100644 docs/source/examples/interactive_tutorial.rst
 create mode 100644 docs/source/examples/local_search.rst
 create mode 100644 docs/source/examples/optuna_backend.rst
 create mode 100644 docs/source/examples/other.rst
 create mode 100644 docs/source/examples/population_based.rst
 create mode 100644 docs/source/examples/sequential_model_based.rst
 create mode 100644 docs/source/examples/sklearn_backend.rst
 create mode 100644 docs/source/faq.rst
 create mode 100644 docs/source/faq/advanced_usage.rst
 create mode 100644 docs/source/faq/common_issues.rst
 create mode 100644 docs/source/faq/getting_help.rst
 create mode 100644 docs/source/faq/getting_started.rst
 create mode 100644 docs/source/faq/integrations.rst
 create mode 100644 docs/source/faq/search_space.rst
 create mode 100644 docs/source/get_involved.rst
 create mode 100644 docs/source/get_involved/code_of_conduct.rst
 create mode 100644 docs/source/get_involved/contributing.rst
 create mode 100644 docs/source/get_started.rst
 create mode 100644 docs/source/index.rst
 create mode 100644 docs/source/installation.rst
 create mode 100644 docs/source/troubleshooting.rst
 create mode 100644 docs/source/troubleshooting/experiments.rst
 create mode 100644 docs/source/troubleshooting/getting_help.rst
 create mode 100644 docs/source/troubleshooting/installation.rst
 create mode 100644 docs/source/troubleshooting/performance.rst
 create mode 100644 docs/source/troubleshooting/results.rst
 create mode 100644 docs/source/troubleshooting/runtime_errors.rst
 create mode 100644 docs/source/user_guide.rst
 create mode 100644 docs/source/user_guide/experiments.rst
 create mode 100644 docs/source/user_guide/integrations.rst
 create mode 100644 docs/source/user_guide/introduction.rst
 create mode 100644 docs/source/user_guide/migration.rst
 create mode 100644 docs/source/user_guide/optimizers.rst
 create mode 100644 docs/source/user_guide/search_spaces.rst
 create mode 100644 docs/tests/__init__.py
 create mode 100644 docs/tests/test_doc_snippets.py

diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 00000000..9ffd37b1
--- /dev/null
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,37 @@
+## Description
+
+<!-- Briefly describe what this PR does -->
+
+## Related Issues
+
+<!-- Link related issues using keywords: "Fixes #123" or "Related to #456" -->
+
+## Type of Change
+
+<!-- Mark the applicable option with [x] -->
+
+- [ ] `[Fix]` - Bug fix (non-breaking change fixing an issue)
+- [ ] `[Feature]` - New feature (non-breaking change adding functionality)
+- [ ] `[Refactor]` - Code refactoring (no functional changes)
+- [ ] `[Release]` - Release preparation
+- [ ] `[Hotfix]` - Critical fix for production
+
+## How was this solved?
+
+<!-- Explain your approach to solving the issue -->
+
+## Checklist
+
+- [ ] PR title includes appropriate tag: `[Fix]`, `[Feature]`, `[Refactor]`, `[Release]`, or `[Hotfix]`
+- [ ] Linked to related issue (if applicable)
+- [ ] Code passes `make check` (lint, format, isort)
+- [ ] Tests added/updated for changes (if applicable)
+- [ ] Documentation updated (if applicable)
+
+## Testing
+
+<!-- How can reviewers verify this change works? -->
+
+## Additional Notes
+
+<!-- Any additional context, screenshots, or considerations -->
diff --git a/.github/auto-label-config.yml b/.github/auto-label-config.yml
new file mode 100644
index 00000000..3ffc2ac0
--- /dev/null
+++ b/.github/auto-label-config.yml
@@ -0,0 +1,41 @@
+# =============================================================================
+# Auto-Label Configuration
+# =============================================================================
+#
+# Used by: auto-label-prs.yml
+#
+# OUTPUT:
+#   PRs get labels based on:
+#   - Title prefix: [Fix] → "Fix" label
+#   - Changed files: docs/** → "documentation" label
+#   - Branch name: dependabot/* → "dependencies" label
+#
+# =============================================================================
+
+autolabeler:
+  - label: 'documentation'
+    files:
+      - 'docs/**/*'
+      - '*.md'
+    branch:
+      - '/docs?\/.+/'
+  - label: 'Fix'
+    title:
+      - '/^\[Fix\]/i'
+  - label: 'Feature'
+    title:
+      - '/^\[Feature\]/i'
+  - label: 'Refactor'
+    title:
+      - '/^\[Refactor\]/i'
+  - label: 'Release'
+    title:
+      - '/^\[Release\]/i'
+  - label: 'Hotfix'
+    title:
+      - '/^\[Hotfix\]/i'
+  - label: 'dependencies'
+    files:
+      - 'pyproject.toml'
+    branch:
+      - '/dependabot\/.+/'
diff --git a/.github/draft-changelog-config.yml b/.github/draft-changelog-config.yml
new file mode 100644
index 00000000..90a0fde0
--- /dev/null
+++ b/.github/draft-changelog-config.yml
@@ -0,0 +1,67 @@
+# =============================================================================
+# Draft Changelog Configuration
+# =============================================================================
+#
+# Used by: draft-changelog.yml
+#
+# OUTPUT:
+#   A draft release in GitHub Releases containing:
+#   - Title: v5.1.0 (based on version-resolver)
+#   - Body: Grouped list of PRs (based on categories)
+#
+# =============================================================================
+
+name-template: 'v$RESOLVED_VERSION'
+tag-template: 'v$RESOLVED_VERSION'
+
+template: |
+  ## What's Changed
+
+  $CHANGES
+
+  **Full Changelog**: https://github.com/$OWNER/$REPOSITORY/compare/$PREVIOUS_TAG...v$RESOLVED_VERSION
+
+categories:
+  - title: 'New Features'
+    labels:
+      - 'Feature'
+      - 'enhancement'
+  - title: 'Bug Fixes'
+    labels:
+      - 'Fix'
+      - 'bug'
+      - 'Hotfix'
+  - title: 'Refactoring'
+    labels:
+      - 'Refactor'
+      - 'refactoring'
+  - title: 'Documentation'
+    labels:
+      - 'documentation'
+      - 'docs'
+  - title: 'Maintenance'
+    labels:
+      - 'maintenance'
+      - 'dependencies'
+      - 'Release'
+
+version-resolver:
+  major:
+    labels:
+      - 'major'
+      - 'breaking'
+  minor:
+    labels:
+      - 'minor'
+      - 'Feature'
+  patch:
+    labels:
+      - 'patch'
+      - 'Fix'
+      - 'Hotfix'
+  default: patch
+
+exclude-labels:
+  - 'skip-changelog'
+  - 'duplicate'
+  - 'invalid'
diff --git a/.github/workflows/auto-label-prs.yml b/.github/workflows/auto-label-prs.yml
new file mode 100644
index 00000000..280c8974
--- /dev/null
+++ b/.github/workflows/auto-label-prs.yml
@@ -0,0 +1,35 @@
+# =============================================================================
+# Auto-Label PRs
+# =============================================================================
+#
+# TRIGGER:
+#   When a PR is opened or updated
+#
+# OUTPUT:
+#   PR gets a label based on its title prefix:
+#   - [Fix] → "Fix" label
+#   - [Feature] → "Feature" label
+#   - [Refactor] → "Refactor" label
+#   - etc.
+#
+# =============================================================================
+
+name: Auto-Label PRs
+
+on:
+  pull_request:
+    types: [opened, reopened, synchronize]
+
+permissions:
+  contents: read
+  pull-requests: write
+
+jobs:
+  add_labels:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: release-drafter/release-drafter@v6
+        with:
+          config-name: auto-label-config.yml
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
new file mode 100644
index 00000000..09d0e4f7
--- /dev/null
+++ b/.github/workflows/docs.yml
@@ -0,0 +1,90 @@
+name: Documentation
+
+on:
+  push:
+    branches:
+      - main
+      - dev
+    paths:
+      - 'docs/**'
+      - 'src/**'
+      - '.github/workflows/docs.yml'
+  pull_request:
+    branches:
+      - main
+      - dev
+    paths:
+      - 'docs/**'
+      - 'src/**'
+      - '.github/workflows/docs.yml'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  test-snippets:
+    name: test-doc-snippets
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ['3.10', '3.11', '3.12']
+      fail-fast: false
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --no-cache-dir --upgrade pip
+          python -m pip install --no-cache-dir -e ".[all_extras,test]"
+          python -m pip install --no-cache-dir -r docs/requirements.txt
+
+      - name: Show dependencies
+        run: python -m pip list
+
+      - name: Test documentation snippets
+        run: |
+          python -m pytest docs/tests/test_doc_snippets.py -v --tb=short
+
+  build-docs:
+    name: build-docs
+    runs-on: ubuntu-latest
+    needs: test-snippets
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --no-cache-dir --upgrade pip
+          python -m pip install --no-cache-dir -e ".[all_extras]"
+          python -m pip install --no-cache-dir -r docs/requirements.txt
+
+      - name: Show dependencies
+        run: python -m pip list
+
+      - name: Build documentation
+        run: |
+          cd docs && sphinx-build -b html source build/html -W --keep-going
+
+      - name: Run doctest
+        run: |
+          cd docs && sphinx-build -b doctest source build/doctest || true
+
+      - name: Upload documentation artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: docs-html
+          path: docs/build/html/
+          retention-days: 7
diff --git a/.github/workflows/draft-changelog.yml b/.github/workflows/draft-changelog.yml
new file mode 100644
index 00000000..b4e245c7
--- /dev/null
+++ b/.github/workflows/draft-changelog.yml
@@ -0,0 +1,39 @@
+# =============================================================================
+# Draft Changelog
+# =============================================================================
+#
+# TRIGGER:
+#   When code is merged/pushed to main
+#
+# OUTPUT:
+#   A draft release in GitHub Releases section containing:
+#   - Grouped list of merged PRs (Features, Bug Fixes, etc.)
+#   - Suggested next version number
+#
+# DOES NOT:
+#   - Publish the release (you must click "Publish" manually)
+#   - Create git tags
+#   - Upload to PyPI
+#
+# =============================================================================
+
+name: Draft Changelog
+
+on:
+  push:
+    branches:
+      - main
+
+permissions:
+  contents: read
+  pull-requests: write
+
+jobs:
+  update_draft:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: release-drafter/release-drafter@v6
+        with:
+          config-name: draft-changelog-config.yml
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 5476c0cf..b8a90b68 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -15,25 +15,118 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
+  # ===========================================================================
+  # STAGE 0: Detect what changed (runs immediately)
+  # ===========================================================================
+  detect-changes:
+    name: detect-changes
+    runs-on: ubuntu-latest
+    outputs:
+      core: ${{ steps.filter.outputs.core }}
+      sklearn: ${{ steps.filter.outputs.sklearn }}
+      sktime: ${{ steps.filter.outputs.sktime }}
+      skpro: ${{ steps.filter.outputs.skpro }}
+      utils: ${{ steps.filter.outputs.utils }}
+      docs: ${{ steps.filter.outputs.docs }}
+      examples: ${{ steps.filter.outputs.examples }}
+      any_src: ${{ steps.filter.outputs.any_src }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Detect changed paths
+        id: filter
+        run: |
+          if [ "${{ github.event_name }}" == "push" ]; then
+            BASE_SHA="${{ github.event.before }}"
+            # Handle initial push where before is all zeros
+            if [ "$BASE_SHA" == "0000000000000000000000000000000000000000" ]; then
+              BASE_SHA="HEAD~1"
+            fi
+          else
+            BASE_SHA="${{ github.event.pull_request.base.sha }}"
+          fi
+
+          CHANGED_FILES=$(git diff --name-only "$BASE_SHA" "${{ github.sha }}" || echo "")
+          echo "Changed files:"
+          echo "$CHANGED_FILES"
+
+          # Core: base module, main hyperactive files, or general tests
+          if echo "$CHANGED_FILES" | grep -qE "^src/hyperactive/(base|tests|__init__|hyperactive)"; then
+            echo "core=true" >> $GITHUB_OUTPUT
+          else
+            echo "core=false" >> $GITHUB_OUTPUT
+          fi
+
+          # sklearn integration
+          if echo "$CHANGED_FILES" | grep -q "^src/hyperactive/integrations/sklearn"; then
+            echo "sklearn=true" >> $GITHUB_OUTPUT
+          else
+            echo "sklearn=false" >> $GITHUB_OUTPUT
+          fi
+
+          # sktime integration
+          if echo "$CHANGED_FILES" | grep -q "^src/hyperactive/integrations/sktime"; then
+            echo "sktime=true" >> $GITHUB_OUTPUT
+          else
+            echo "sktime=false" >> $GITHUB_OUTPUT
+          fi
+
+          # skpro integration
+          if echo "$CHANGED_FILES" | grep -q "^src/hyperactive/integrations/skpro"; then
+            echo "skpro=true" >> $GITHUB_OUTPUT
+          else
+            echo "skpro=false" >> $GITHUB_OUTPUT
+          fi
+
+          # utils
+          if echo "$CHANGED_FILES" | grep -q "^src/hyperactive/utils"; then
+            echo "utils=true" >> $GITHUB_OUTPUT
+          else
+            echo "utils=false" >> $GITHUB_OUTPUT
+          fi
+
+          # docs
+          if echo "$CHANGED_FILES" | grep -qE "^docs/|\.rst$|\.md$"; then
+            echo "docs=true" >> $GITHUB_OUTPUT
+          else
+            echo "docs=false" >> $GITHUB_OUTPUT
+          fi
+
+          # examples
+          if echo "$CHANGED_FILES" | grep -q "^examples/"; then
+            echo "examples=true" >> $GITHUB_OUTPUT
+          else
+            echo "examples=false" >> $GITHUB_OUTPUT
+          fi
+
+          # any source code
+          if echo "$CHANGED_FILES" | grep -q "^src/"; then
+            echo "any_src=true" >> $GITHUB_OUTPUT
+          else
+            echo "any_src=false" >> $GITHUB_OUTPUT
+          fi
+
+  # ===========================================================================
+  # STAGE 1: Fast checks (code-quality)
+  # ===========================================================================
   code-quality:
     name: code-quality
     runs-on: ubuntu-latest
     steps:
-      - name: repository checkout step
-        uses: actions/checkout@v5
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
 
-      - name: python environment step
+      - name: Set up Python 3.11
         uses: actions/setup-python@v5
         with:
           python-version: "3.11"
 
-      - name: install pre-commit
-        run: python3 -m pip install pre-commit
-
-      - name: Checkout code
-        uses: actions/checkout@v5
-        with:
-          fetch-depth: 0
+      - name: Install pre-commit
+        run: python -m pip install --no-cache-dir pre-commit
 
       - name: Get changed files
         id: changed-files
@@ -53,8 +146,162 @@ jobs:
             echo "No changed files to check."
           fi
 
+  # ===========================================================================
+  # STAGE 2: Targeted tests (only for changed areas, fast feedback)
+  # ===========================================================================
+  targeted-core:
+    name: targeted-core
+    needs: [code-quality, detect-changes]
+    if: needs.detect-changes.outputs.core == 'true' || needs.detect-changes.outputs.any_src == 'false'
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --no-cache-dir --upgrade pip
+          python -m pip install --no-cache-dir -e .[test]
+
+      - name: Run core tests
+        run: |
+          python -m pytest src/hyperactive/base/tests/ src/hyperactive/tests/ -v -p no:warnings
+
+  targeted-sklearn:
+    name: targeted-sklearn
+    needs: [code-quality, detect-changes]
+    if: needs.detect-changes.outputs.sklearn == 'true'
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --no-cache-dir --upgrade pip
+          python -m pip install --no-cache-dir -e .[all_extras,test]
+
+      - name: Run sklearn integration tests
+        run: |
+          python -m pytest src/hyperactive/integrations/sklearn/tests/ -v -p no:warnings
+
+  targeted-sktime:
+    name: targeted-sktime
+    needs: [code-quality, detect-changes]
+    if: needs.detect-changes.outputs.sktime == 'true'
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --no-cache-dir --upgrade pip
+          python -m pip install --no-cache-dir -e .[sktime-integration,test]
+
+      - name: Run sktime integration tests
+        run: |
+          python -m pytest src/hyperactive/integrations/sktime/tests/ -v -p no:warnings
+
+  targeted-skpro:
+    name: targeted-skpro
+    needs: [code-quality, detect-changes]
+    if: needs.detect-changes.outputs.skpro == 'true'
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --no-cache-dir --upgrade pip
+          python -m pip install --no-cache-dir -e .[sktime-integration,test]
+
+      - name: Run skpro integration tests
+        run: |
+          python -m pytest src/hyperactive/integrations/skpro/tests/ -v -p no:warnings
+
+  targeted-utils:
+    name: targeted-utils
+    needs: [code-quality, detect-changes]
+    if: needs.detect-changes.outputs.utils == 'true'
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --no-cache-dir --upgrade pip
+          python -m pip install --no-cache-dir -e .[test,test_parallel_backends]
+
+      - name: Run utils tests
+        run: |
+          python -m pytest src/hyperactive/utils/tests/ -v -p no:warnings
+
+  # Gate job: passes if all targeted tests pass OR are skipped (not failed)
+  targeted-tests-gate:
+    name: targeted-tests-gate
+    needs: [targeted-core, targeted-sklearn, targeted-sktime, targeted-skpro, targeted-utils]
+    if: always()
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check targeted test results
+        run: |
+          echo "Core: ${{ needs.targeted-core.result }}"
+          echo "Sklearn: ${{ needs.targeted-sklearn.result }}"
+          echo "Sktime: ${{ needs.targeted-sktime.result }}"
+          echo "Skpro: ${{ needs.targeted-skpro.result }}"
+          echo "Utils: ${{ needs.targeted-utils.result }}"
+
+          # Fail if any targeted test failed (not skipped)
+          if [[ "${{ needs.targeted-core.result }}" == "failure" ]] || \
+             [[ "${{ needs.targeted-sklearn.result }}" == "failure" ]] || \
+             [[ "${{ needs.targeted-sktime.result }}" == "failure" ]] || \
+             [[ "${{ needs.targeted-skpro.result }}" == "failure" ]] || \
+             [[ "${{ needs.targeted-utils.result }}" == "failure" ]]; then
+            echo "One or more targeted tests failed"
+            exit 1
+          fi
+          echo "All targeted tests passed or were skipped"
+
+  # ===========================================================================
+  # STAGE 3: Full test matrix (only after targeted tests pass)
+  # ===========================================================================
   test-no-extras:
     name: test-no-extras
+    needs: [targeted-tests-gate]
+    if: always() && needs.targeted-tests-gate.result == 'success'
     strategy:
       matrix:
         python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
@@ -78,9 +325,8 @@ jobs:
 
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
-          python -m pip install build
-
+          python -m pip install --no-cache-dir --upgrade pip
+          python -m pip install --no-cache-dir build
           make install-no-extras-for-test
 
       - name: Show dependencies
@@ -92,6 +338,8 @@ jobs:
 
   test-all-extras:
     name: test-all-extras
+    needs: [targeted-tests-gate]
+    if: always() && needs.targeted-tests-gate.result == 'success'
     strategy:
       matrix:
         python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
@@ -115,9 +363,8 @@ jobs:
 
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
-          python -m pip install build
-
+          python -m pip install --no-cache-dir --upgrade pip
+          python -m pip install --no-cache-dir build
           make install-all-extras-for-test
 
       - name: Show dependencies
@@ -129,6 +376,8 @@ jobs:
 
   test-sklearn-versions:
     name: test-sklearn-${{ matrix.sklearn-version }}-python-${{ matrix.python-version }}
+    needs: [targeted-tests-gate]
+    if: always() && needs.targeted-tests-gate.result == 'success'
     runs-on: ubuntu-latest
 
     strategy:
@@ -145,20 +394,77 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
 
-      - name: Install dependencies for scikit-learn ${{ matrix.sklearn-version }}
+      - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
-          python -m pip install .[all_extras,test] scikit-learn==${{ matrix.sklearn-version }}.*
+          python -m pip install --no-cache-dir --upgrade pip
+          python -m pip install --no-cache-dir -e .[all_extras,test] scikit-learn==${{ matrix.sklearn-version }}.*
 
       - name: Show dependencies
         run: python -m pip list
 
-      - name: Run sklearn integration tests for ${{ matrix.sklearn-version }}
+      - name: Run sklearn integration tests
         run: |
           python -m pytest -x -p no:warnings src/hyperactive/integrations/sklearn/
 
+  coverage:
+    name: coverage
+    needs: [targeted-tests-gate]
+    if: always() && needs.targeted-tests-gate.result == 'success'
+    runs-on: ubuntu-latest
+    timeout-minutes: 15
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --no-cache-dir --upgrade pip
+          python -m pip install --no-cache-dir -e .[all_extras,test,test_parallel_backends,sktime-integration]
+
+      - name: Run tests with coverage
+        run: |
+          python -m pytest src/hyperactive/ --cov=src/hyperactive --cov-report=xml --cov-report=term -p no:warnings
+
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v5
+        with:
+          files: ./coverage.xml
+          fail_ci_if_error: false
+          verbose: true
+
+  doctest-examples:
+    name: doctest-examples
+    needs: [targeted-tests-gate]
+    if: always() && needs.targeted-tests-gate.result == 'success'
+    runs-on: ubuntu-latest
+    timeout-minutes: 20
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --no-cache-dir --upgrade pip
+          python -m pip install --no-cache-dir -e .[all_extras,test,sktime-integration]
+
+      - name: Run docstring example tests
+        run: |
+          python -m pytest --doctest-modules src/hyperactive/ -v -p no:warnings
+
   test-examples:
     name: test-examples
+    needs: [targeted-tests-gate]
+    if: always() && needs.targeted-tests-gate.result == 'success'
     runs-on: ubuntu-latest
     timeout-minutes: 15
 
@@ -195,8 +501,8 @@ jobs:
       - name: Install dependencies
         if: steps.check-examples.outputs.examples_changed == 'true'
         run: |
-          python -m pip install --upgrade pip
-          python -m pip install build
+          python -m pip install --no-cache-dir --upgrade pip
+          python -m pip install --no-cache-dir build
           make install-all-extras-for-test
 
       - name: Show dependencies
diff --git a/.gitignore b/.gitignore
index 5bc0c596..3e7b58f9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -32,7 +32,6 @@ MANIFEST
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 *.manifest
 *.spec
-requirements*.txt
 
 # Installer logs
 pip-log.txt
@@ -75,6 +74,8 @@ instance/
 
 # Sphinx documentation
 docs/_build/
+docs/build/
+docs/source/api_reference/auto_generated/
 
 # PyBuilder
 target/
diff --git a/.readthedocs.yml b/.readthedocs.yml
new file mode 100644
index 00000000..ef2552cd
--- /dev/null
+++ b/.readthedocs.yml
@@ -0,0 +1,19 @@
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+version: 2
+
+python:
+  install:
+    - method: pip
+      path: .
+      extra_requirements:
+        - docs
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.12"
+
+sphinx:
+  configuration: docs/source/conf.py
diff --git a/Makefile b/Makefile
index 3801fe58..73cced08 100644
--- a/Makefile
+++ b/Makefile
@@ -78,19 +78,19 @@ uninstall:
 	rm -fr build dist *.egg-info
 
 install-test-requirements:
-	python -m pip install .[test]
+	python -m pip install --no-cache-dir .[test]
 
 install-build-requirements:
-	python -m pip install .[build]
+	python -m pip install --no-cache-dir .[build]
 
 install-all-extras:
-	python -m pip install .[all_extras]
+	python -m pip install --no-cache-dir .[all_extras]
 
 install-no-extras-for-test:
-	python -m pip install .[test]
+	python -m pip install --no-cache-dir .[test]
 
 install-all-extras-for-test:
-	python -m pip install .[all_extras,test,test_parallel_backends,sktime-integration]
+	python -m pip install --no-cache-dir .[all_extras,test,test_parallel_backends,sktime-integration]
 
 install-editable:
 	pip install -e .
diff --git a/README.md b/README.md
index 13dcf1e6..195c37ec 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ package alongside direct interfaces to Optuna and scikit-learn optimizers, suppo
 
 ---
 
-| | [Overview](https://github.com/SimonBlanke/Hyperactive#overview) • [Installation](https://github.com/SimonBlanke/Hyperactive#installation) • [Tutorial](https://nbviewer.org/github/SimonBlanke/hyperactive-tutorial/blob/main/notebooks/hyperactive_tutorial.ipynb) • [API reference](https://simonblanke.github.io/hyperactive-documentation/5.0/) • [Citation](https://github.com/SimonBlanke/Hyperactive#citing-hyperactive) |
+| | [Overview](https://github.com/SimonBlanke/Hyperactive#overview) • [Installation](https://github.com/SimonBlanke/Hyperactive#installation) • [Tutorial](https://nbviewer.org/github/SimonBlanke/hyperactive-tutorial/blob/main/notebooks/hyperactive_tutorial.ipynb) • [API reference](https://hyperactive.readthedocs.io/en/latest/#) • [Citation](https://github.com/SimonBlanke/Hyperactive#citing-hyperactive) |
 |---|---|
 | **Open&#160;Source** | [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![GC.OS Sponsored](https://img.shields.io/badge/GC.OS-Sponsored%20Project-orange.svg?style=flat&colorA=0eac92&colorB=2077b4)](https://gc-os-ai.github.io/) |
 | **Community** | [![Discord](https://img.shields.io/static/v1?logo=discord&label=Discord&message=chat&color=lightgreen)](https://discord.gg/7uKdHfdcJG) [![LinkedIn](https://img.shields.io/static/v1?logo=linkedin&label=LinkedIn&message=news&color=lightblue)](https://www.linkedin.com/company/german-center-for-open-source-ai)  |
diff --git a/docs/README.md b/docs/README.md
new file mode 100644
index 00000000..aaf9f71a
--- /dev/null
+++ b/docs/README.md
@@ -0,0 +1,92 @@
+# Hyperactive Documentation
+
+This directory contains the Sphinx-based documentation for Hyperactive.
+
+## Building the Documentation
+
+### Prerequisites
+
+Install the required dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+You'll also need to have Hyperactive installed:
+
+```bash
+pip install -e ..  # Install Hyperactive in development mode from parent directory
+```
+
+### Building HTML Documentation
+
+From the `source` directory:
+
+```bash
+cd source
+make clean  # Clean previous builds
+make html   # Build HTML documentation
+```
+
+The built documentation will be in `build/html/`. Open `build/html/index.html` in your browser to view.
+
+### Live Preview with Auto-Rebuild
+
+For development, you can use auto-rebuild mode:
+
+```bash
+cd source
+make autobuild
+```
+
+This will start a local server (typically at http://127.0.0.1:8000) that automatically rebuilds when you make changes to the documentation source files.
+
+## Documentation Structure
+
+- `source/` - Documentation source files
+  - `conf.py` - Sphinx configuration
+  - `index.rst` - Main landing page
+  - `api_reference/` - API reference documentation (auto-generated)
+  - `user_guide/` - User guide pages (currently stubs)
+  - `examples/` - Example notebooks and galleries (currently stubs)
+  - `get_involved/` - Contributing guidelines (currently stubs)
+  - `about/` - About pages (currently stubs)
+  - `_templates/` - Custom Sphinx templates
+  - `_static/` - Static files (CSS, images, etc.)
+- `build/` - Built documentation (generated, not tracked in git)
+
+## Current Status
+
+The documentation is currently set up with:
+
+- ✅ Full API reference auto-generated from docstrings
+- ✅ Sphinx configuration following SK-Time's approach
+- ✅ pydata_sphinx_theme for consistent look with scientific Python ecosystem
+- ✅ Structural placeholders for future static content
+
+Static pages (User Guide, Examples, etc.) are currently placeholder stubs marked "under construction" that can be filled in later.
+
+## Adding New Content
+
+### API Reference
+
+The API reference is automatically generated from docstrings. To update:
+
+1. Ensure your class/function has proper NumPy-style docstrings
+2. Add the class/function to the appropriate `api_reference/*.rst` file using the `autosummary` directive
+3. Rebuild the documentation
+
+### Static Pages
+
+To add content to the placeholder pages:
+
+1. Edit the corresponding `.rst` or `.md` file in the appropriate directory
+2. Remove the "under construction" note
+3. Add your content using reStructuredText or Markdown syntax
+4. Rebuild to see your changes
+
+## Notes
+
+- All API documentation is 100% auto-generated from source code docstrings
+- The structure allows for easy addition of static content in the future
+- Build warnings about missing references are normal during early development
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 00000000..3cf5a52e
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,19 @@
+# Requirements for building Hyperactive documentation
+
+# Core Sphinx and extensions
+sphinx>=7.0.0
+sphinx-autobuild
+sphinx-copybutton
+sphinx-design
+sphinx-issues
+myst-parser
+numpydoc
+
+# Theme
+pydata-sphinx-theme
+
+# For intersphinx linking
+# These need to be importable to build docs
+numpy
+pandas
+scikit-learn
diff --git a/docs/source/Makefile b/docs/source/Makefile
new file mode 100644
index 00000000..46aa97ff
--- /dev/null
+++ b/docs/source/Makefile
@@ -0,0 +1,31 @@
+# Minimal makefile for Sphinx documentation
+
+# You can set these variables from the command line.
+SPHINXBUILD        = sphinx-build
+SPHINXOPTS         = -j auto
+SPHINXAUTOBUILD    = sphinx-autobuild
+SPHINXAUTOOPTS     = -j auto
+SOURCEDIR          = .
+BUILDDIR           = ../build
+HYPERACTIVEDIR     = ../../src/hyperactive
+
+.PHONY: help build autobuild
+
+# "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)"
+
+clean:
+	rm -rf $(BUILDDIR)
+	rm -rf api_reference/auto_generated
+	@echo "Deleted directory $(BUILDDIR) and auto_generated files."
+
+# $(O) is meant as a shortcut for custom options.
+# i.e to log stderr into a separate file:
+# make build O="--no-color 2> build_warnings.log"
+html:
+	$(SPHINXBUILD) -M html "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+# $(O) is meant as a shortcut for custom options.
+autobuild:
+	$(SPHINXAUTOBUILD) "$(SOURCEDIR)" "$(BUILDDIR)/html" -d "$(BUILDDIR)/doctrees" $(SPHINXAUTOOPTS) $(O) --watch "$(HYPERACTIVEDIR)" --re-ignore ".*\.json"
diff --git a/docs/source/_snippets/__init__.py b/docs/source/_snippets/__init__.py
new file mode 100644
index 00000000..e69a90c9
--- /dev/null
+++ b/docs/source/_snippets/__init__.py
@@ -0,0 +1,12 @@
+"""Documentation code snippets.
+
+This package contains testable Python code snippets that are included in the
+documentation using Sphinx's ``literalinclude`` directive. Each snippet file
+can be executed directly to verify it works correctly.
+
+The snippets are organized by documentation section:
+- getting_started/: Quick start examples
+- installation/: Installation verification examples
+- user_guide/: In-depth tutorial examples
+- examples/: Gallery examples
+"""
diff --git a/docs/source/_snippets/conftest.py b/docs/source/_snippets/conftest.py
new file mode 100644
index 00000000..0994864b
--- /dev/null
+++ b/docs/source/_snippets/conftest.py
@@ -0,0 +1,46 @@
+"""Pytest configuration for documentation snippets.
+
+This conftest provides shared fixtures that snippet files can use for testing.
+The fixtures ensure consistent behavior across all snippet tests.
+"""
+
+import numpy as np
+import pytest
+
+
+@pytest.fixture
+def simple_search_space():
+    """Simple search space for basic examples."""
+    return {
+        "x": np.arange(-5, 5, 0.1),
+        "y": np.arange(-5, 5, 0.1),
+    }
+
+
+@pytest.fixture
+def simple_objective():
+    """Simple objective function for basic examples."""
+
+    def objective(params):
+        x = params["x"]
+        y = params["y"]
+        return -(x**2 + y**2)
+
+    return objective
+
+
+@pytest.fixture
+def sklearn_data():
+    """Load iris dataset for sklearn examples."""
+    from sklearn.datasets import load_iris
+
+    return load_iris(return_X_y=True)
+
+
+@pytest.fixture
+def sklearn_train_test_split(sklearn_data):
+    """Split sklearn data into train and test sets."""
+    from sklearn.model_selection import train_test_split
+
+    X, y = sklearn_data
+    return train_test_split(X, y, test_size=0.2, random_state=42)
diff --git a/docs/source/_snippets/examples/__init__.py b/docs/source/_snippets/examples/__init__.py
new file mode 100644
index 00000000..581dc357
--- /dev/null
+++ b/docs/source/_snippets/examples/__init__.py
@@ -0,0 +1 @@
+"""Example gallery code snippets for documentation."""
diff --git a/docs/source/_snippets/examples/advanced_examples.py b/docs/source/_snippets/examples/advanced_examples.py
new file mode 100644
index 00000000..8f2627df
--- /dev/null
+++ b/docs/source/_snippets/examples/advanced_examples.py
@@ -0,0 +1,77 @@
+"""Advanced examples for the examples.rst page.
+
+This snippet file contains runnable examples demonstrating Hyperactive's
+advanced functionality like warm starting and optimizer comparison.
+"""
+
+from sklearn.datasets import load_wine
+from sklearn.ensemble import RandomForestClassifier
+
+from hyperactive.experiment.integrations import SklearnCvExperiment
+from hyperactive.opt.gfo import HillClimbing
+
+# Setup common fixtures for examples
+X, y = load_wine(return_X_y=True)
+experiment = SklearnCvExperiment(
+    estimator=RandomForestClassifier(random_state=42),
+    X=X,
+    y=y,
+    cv=3,
+)
+search_space = {
+    "n_estimators": list(range(10, 101, 10)),
+    "max_depth": list(range(1, 11)),
+    "min_samples_split": list(range(2, 11)),
+}
+
+
+# [start:warm_starting]
+
+# Previous best parameters
+warm_start_points = [
+    {"n_estimators": 100, "max_depth": 10, "min_samples_split": 5},
+]
+
+optimizer = HillClimbing(
+    search_space=search_space,
+    n_iter=40,
+    experiment=experiment,
+    initialize={"warm_start": warm_start_points},
+)
+best_params = optimizer.solve()
+# [end:warm_starting]
+
+
+# [start:comparing_optimizers]
+from hyperactive.opt.gfo import (
+    BayesianOptimizer,
+    HillClimbing,
+    ParticleSwarmOptimizer,
+    RandomSearch,
+)
+
+optimizers = {
+    "HillClimbing": HillClimbing,
+    "RandomSearch": RandomSearch,
+    "Bayesian": BayesianOptimizer,
+    "ParticleSwarm": ParticleSwarmOptimizer,
+}
+
+results = {}
+for name, OptClass in optimizers.items():
+    optimizer = OptClass(
+        search_space=search_space,
+        n_iter=50,
+        experiment=experiment,
+        random_state=42,
+    )
+    best = optimizer.solve()
+    score, _ = experiment.score(best)
+    results[name] = {"params": best, "score": score}
+    print(f"{name}: score={score:.4f}")
+# [end:comparing_optimizers]
+
+
+if __name__ == "__main__":
+    print("Advanced examples passed!")
+    print(f"Best optimizer results: {results}")
diff --git a/docs/source/_snippets/examples/basic_examples.py b/docs/source/_snippets/examples/basic_examples.py
new file mode 100644
index 00000000..c6bff6ba
--- /dev/null
+++ b/docs/source/_snippets/examples/basic_examples.py
@@ -0,0 +1,73 @@
+"""Basic examples for the examples.rst page.
+
+This snippet file contains runnable examples demonstrating Hyperactive's
+basic functionality including custom function and sklearn optimization.
+"""
+
+import numpy as np
+
+# [start:custom_function]
+from hyperactive.opt.gfo import HillClimbing
+
+
+def objective(params):
+    x = params["x"]
+    y = params["y"]
+    return -(x**2 + y**2)  # Maximize (minimize the parabola)
+
+
+search_space = {
+    "x": np.arange(-5, 5, 0.1),
+    "y": np.arange(-5, 5, 0.1),
+}
+
+optimizer = HillClimbing(
+    search_space=search_space,
+    n_iter=100,
+    experiment=objective,
+)
+best_params = optimizer.solve()
+print(f"Best parameters: {best_params}")
+# [end:custom_function]
+
+
+# [start:sklearn_tuning]
+from sklearn.datasets import load_wine
+from sklearn.ensemble import RandomForestClassifier
+
+from hyperactive.experiment.integrations import SklearnCvExperiment
+from hyperactive.opt.gfo import HillClimbing
+
+# Load data
+X, y = load_wine(return_X_y=True)
+
+# Create experiment
+experiment = SklearnCvExperiment(
+    estimator=RandomForestClassifier(random_state=42),
+    X=X,
+    y=y,
+    cv=3,
+)
+
+# Define search space
+search_space = {
+    "n_estimators": list(range(10, 201)),
+    "max_depth": list(range(1, 21)),
+    "min_samples_split": list(range(2, 21)),
+    "min_samples_leaf": list(range(1, 11)),
+}
+
+# Optimize
+optimizer = HillClimbing(
+    search_space=search_space,
+    n_iter=40,
+    random_state=42,
+    experiment=experiment,
+)
+best_params = optimizer.solve()
+# [end:sklearn_tuning]
+
+
+if __name__ == "__main__":
+    print("Basic examples passed!")
+    print(f"Custom function best: {best_params}")
diff --git a/docs/source/_snippets/getting_started/__init__.py b/docs/source/_snippets/getting_started/__init__.py
new file mode 100644
index 00000000..d3edc1cc
--- /dev/null
+++ b/docs/source/_snippets/getting_started/__init__.py
@@ -0,0 +1 @@
+"""Getting started code snippets for documentation."""
diff --git a/docs/source/_snippets/getting_started/bayesian_optimizer.py b/docs/source/_snippets/getting_started/bayesian_optimizer.py
new file mode 100644
index 00000000..db8cf2b0
--- /dev/null
+++ b/docs/source/_snippets/getting_started/bayesian_optimizer.py
@@ -0,0 +1,41 @@
+"""Bayesian Optimizer example for documentation.
+
+This snippet demonstrates the usage of BayesianOptimizer for
+optimization problems. It is included in get_started.rst.
+"""
+
+# [start:full_example]
+# [end:full_example]
+# Need to define experiment and search_space for standalone execution
+import numpy as np
+
+from hyperactive.opt.gfo import BayesianOptimizer
+
+
+def experiment(params):
+    """Simple objective function."""
+    x = params["x"]
+    y = params["y"]
+    return -(x**2 + y**2)
+
+
+search_space = {
+    "x": np.arange(-5, 5, 0.1),
+    "y": np.arange(-5, 5, 0.1),
+}
+
+# [start:optimizer_usage]
+optimizer = BayesianOptimizer(
+    search_space=search_space,
+    n_iter=30,
+    experiment=experiment,
+)
+best_params = optimizer.solve()
+# [end:optimizer_usage]
+
+if __name__ == "__main__":
+    print(f"Best parameters: {best_params}")
+    # Verify the optimization found parameters close to (0, 0)
+    assert abs(best_params["x"]) < 2.0, f"Expected x near 0, got {best_params['x']}"
+    assert abs(best_params["y"]) < 2.0, f"Expected y near 0, got {best_params['y']}"
+    print("Bayesian optimizer example passed!")
diff --git a/docs/source/_snippets/getting_started/index_bayesian.py b/docs/source/_snippets/getting_started/index_bayesian.py
new file mode 100644
index 00000000..98cc7e09
--- /dev/null
+++ b/docs/source/_snippets/getting_started/index_bayesian.py
@@ -0,0 +1,37 @@
+"""Bayesian optimization example for index page.
+
+This snippet demonstrates Bayesian optimization with a more complex
+objective function shown on the landing page. It is included in index.rst.
+"""
+
+# [start:full_example]
+import numpy as np
+
+from hyperactive.opt.gfo import BayesianOptimizer
+
+
+def complex_objective(params):
+    x = params["x"]
+    y = params["y"]
+    return -((x - 2) ** 2 + (y + 1) ** 2) + np.sin(x * y)
+
+
+search_space = {
+    "x": np.linspace(-5, 5, 100),
+    "y": np.linspace(-5, 5, 100),
+}
+
+optimizer = BayesianOptimizer(
+    search_space=search_space,
+    n_iter=50,
+    experiment=complex_objective,
+)
+best_params = optimizer.solve()
+# [end:full_example]
+
+if __name__ == "__main__":
+    print(f"Best parameters: {best_params}")
+    # Verify we got valid parameters
+    assert "x" in best_params
+    assert "y" in best_params
+    print("Index Bayesian example passed!")
diff --git a/docs/source/_snippets/getting_started/index_custom_function.py b/docs/source/_snippets/getting_started/index_custom_function.py
new file mode 100644
index 00000000..ca7a9e24
--- /dev/null
+++ b/docs/source/_snippets/getting_started/index_custom_function.py
@@ -0,0 +1,39 @@
+"""Custom function example for index page.
+
+This snippet demonstrates the basic custom function optimization
+shown on the landing page. It is included in index.rst.
+"""
+
+# [start:full_example]
+import numpy as np
+
+from hyperactive.opt.gfo import HillClimbing
+
+
+# Define your objective function
+def objective(params):
+    x, y = params["x"], params["y"]
+    return -(x**2 + y**2)  # Maximize (minimize negative)
+
+
+# Define the search space
+search_space = {
+    "x": np.arange(-5, 5, 0.1),
+    "y": np.arange(-5, 5, 0.1),
+}
+
+# Create optimizer and solve
+optimizer = HillClimbing(
+    search_space=search_space,
+    n_iter=100,
+    experiment=objective,
+)
+best_params = optimizer.solve()
+print(f"Best parameters: {best_params}")
+# [end:full_example]
+
+if __name__ == "__main__":
+    # Verify the optimization found parameters close to (0, 0)
+    assert abs(best_params["x"]) < 1.0, f"Expected x near 0, got {best_params['x']}"
+    assert abs(best_params["y"]) < 1.0, f"Expected y near 0, got {best_params['y']}"
+    print("Index custom function example passed!")
diff --git a/docs/source/_snippets/getting_started/index_sklearn_tuning.py b/docs/source/_snippets/getting_started/index_sklearn_tuning.py
new file mode 100644
index 00000000..6080c43c
--- /dev/null
+++ b/docs/source/_snippets/getting_started/index_sklearn_tuning.py
@@ -0,0 +1,35 @@
+"""Scikit-learn tuning example for index page.
+
+This snippet demonstrates sklearn integration using OptCV
+shown on the landing page. It is included in index.rst.
+"""
+
+# [start:full_example]
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+
+from hyperactive.integrations.sklearn import OptCV
+from hyperactive.opt.gfo import HillClimbing
+
+# Load data
+X, y = load_iris(return_X_y=True)
+X_train, X_test, y_train, y_test = train_test_split(X, y)
+
+# Define optimizer with search space
+search_space = {"kernel": ["linear", "rbf"], "C": [0.1, 1, 10]}
+optimizer = HillClimbing(search_space=search_space, n_iter=20)
+
+# Create tuned estimator and fit
+tuned_svc = OptCV(SVC(), optimizer)
+tuned_svc.fit(X_train, y_train)
+
+print(f"Best params: {tuned_svc.best_params_}")
+# [end:full_example]
+
+if __name__ == "__main__":
+    # Verify we got valid results
+    assert hasattr(tuned_svc, "best_params_")
+    assert "kernel" in tuned_svc.best_params_
+    assert "C" in tuned_svc.best_params_
+    print("Index sklearn tuning example passed!")
diff --git a/docs/source/_snippets/getting_started/quick_start.py b/docs/source/_snippets/getting_started/quick_start.py
new file mode 100644
index 00000000..7f342fce
--- /dev/null
+++ b/docs/source/_snippets/getting_started/quick_start.py
@@ -0,0 +1,41 @@
+"""Quick start example for documentation.
+
+This snippet demonstrates the basic usage of Hyperactive for optimizing
+a custom objective function. It is included in get_started.rst.
+"""
+
+# [start:full_example]
+import numpy as np
+
+from hyperactive.opt.gfo import HillClimbing
+
+
+# 1. Define your objective function
+def objective(params):
+    x = params["x"]
+    y = params["y"]
+    return -(x**2 + y**2)  # Hyperactive maximizes by default
+
+
+# 2. Define the search space
+search_space = {
+    "x": np.arange(-5, 5, 0.1),
+    "y": np.arange(-5, 5, 0.1),
+}
+
+# 3. Create an optimizer and solve
+optimizer = HillClimbing(
+    search_space=search_space,
+    n_iter=100,
+    experiment=objective,
+)
+best_params = optimizer.solve()
+
+print(f"Best parameters: {best_params}")
+# [end:full_example]
+
+if __name__ == "__main__":
+    # Verify the optimization found parameters close to (0, 0)
+    assert abs(best_params["x"]) < 1.0, f"Expected x near 0, got {best_params['x']}"
+    assert abs(best_params["y"]) < 1.0, f"Expected y near 0, got {best_params['y']}"
+    print("Quick start example passed!")
diff --git a/docs/source/_snippets/getting_started/sklearn_optcv.py b/docs/source/_snippets/getting_started/sklearn_optcv.py
new file mode 100644
index 00000000..307ea2b3
--- /dev/null
+++ b/docs/source/_snippets/getting_started/sklearn_optcv.py
@@ -0,0 +1,41 @@
+"""Scikit-learn OptCV wrapper example for documentation.
+
+This snippet demonstrates how to use OptCV as a drop-in replacement
+for GridSearchCV. It is included in get_started.rst.
+"""
+
+# [start:full_example]
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+
+from hyperactive.integrations.sklearn import OptCV
+from hyperactive.opt.gfo import HillClimbing
+
+# Load and split data
+X, y = load_iris(return_X_y=True)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+# Define optimizer with search space
+search_space = {"kernel": ["linear", "rbf"], "C": [0.1, 1, 10, 100]}
+optimizer = HillClimbing(search_space=search_space, n_iter=20)
+
+# Create tuned estimator (like GridSearchCV)
+tuned_svc = OptCV(SVC(), optimizer)
+
+# Fit and predict as usual
+tuned_svc.fit(X_train, y_train)
+y_pred = tuned_svc.predict(X_test)
+
+# Access results
+print(f"Best params: {tuned_svc.best_params_}")
+print(f"Best estimator: {tuned_svc.best_estimator_}")
+# [end:full_example]
+
+if __name__ == "__main__":
+    # Verify we got valid results
+    assert hasattr(tuned_svc, "best_params_")
+    assert hasattr(tuned_svc, "best_estimator_")
+    assert "kernel" in tuned_svc.best_params_
+    assert "C" in tuned_svc.best_params_
+    print("Sklearn OptCV example passed!")
diff --git a/docs/source/_snippets/getting_started/sklearn_random_forest.py b/docs/source/_snippets/getting_started/sklearn_random_forest.py
new file mode 100644
index 00000000..10d0d1db
--- /dev/null
+++ b/docs/source/_snippets/getting_started/sklearn_random_forest.py
@@ -0,0 +1,48 @@
+"""Scikit-learn RandomForest example for documentation.
+
+This snippet demonstrates how to optimize a RandomForest classifier
+using Hyperactive's SklearnCvExperiment. It is included in get_started.rst.
+"""
+
+# [start:full_example]
+from sklearn.datasets import load_iris
+from sklearn.ensemble import RandomForestClassifier
+
+from hyperactive.experiment.integrations import SklearnCvExperiment
+from hyperactive.opt.gfo import HillClimbing
+
+# Load data
+X, y = load_iris(return_X_y=True)
+
+# Create an experiment that handles cross-validation
+experiment = SklearnCvExperiment(
+    estimator=RandomForestClassifier(random_state=42),
+    X=X,
+    y=y,
+    cv=5,
+)
+
+# Define hyperparameter search space
+search_space = {
+    "n_estimators": list(range(10, 200, 10)),
+    "max_depth": list(range(1, 20)),
+    "min_samples_split": list(range(2, 10)),
+}
+
+# Optimize
+optimizer = HillClimbing(
+    search_space=search_space,
+    n_iter=50,
+    experiment=experiment,
+)
+best_params = optimizer.solve()
+
+print(f"Best hyperparameters: {best_params}")
+# [end:full_example]
+
+if __name__ == "__main__":
+    # Verify we got valid hyperparameters
+    assert "n_estimators" in best_params
+    assert "max_depth" in best_params
+    assert "min_samples_split" in best_params
+    print("Sklearn RandomForest example passed!")
diff --git a/docs/source/_snippets/installation/__init__.py b/docs/source/_snippets/installation/__init__.py
new file mode 100644
index 00000000..3cee52a4
--- /dev/null
+++ b/docs/source/_snippets/installation/__init__.py
@@ -0,0 +1 @@
+"""Installation code snippets for documentation."""
diff --git a/docs/source/_snippets/installation/verify_installation.py b/docs/source/_snippets/installation/verify_installation.py
new file mode 100644
index 00000000..5a97971c
--- /dev/null
+++ b/docs/source/_snippets/installation/verify_installation.py
@@ -0,0 +1,32 @@
+"""Installation verification snippet.
+
+This snippet demonstrates how to verify Hyperactive installation.
+"""
+
+# [start:verify_installation]
+import hyperactive
+
+print(f"Hyperactive version: {hyperactive.__version__}")
+
+# Quick test
+import numpy as np
+
+from hyperactive.opt.gfo import HillClimbing
+
+
+def objective(params):
+    return -(params["x"] ** 2)
+
+
+optimizer = HillClimbing(
+    search_space={"x": np.arange(-5, 5, 0.1)},
+    n_iter=10,
+    experiment=objective,
+)
+best = optimizer.solve()
+print(f"Test optimization successful: {best}")
+# [end:verify_installation]
+
+
+if __name__ == "__main__":
+    print("Installation verification passed!")
diff --git a/docs/source/_snippets/user_guide/__init__.py b/docs/source/_snippets/user_guide/__init__.py
new file mode 100644
index 00000000..41675fba
--- /dev/null
+++ b/docs/source/_snippets/user_guide/__init__.py
@@ -0,0 +1 @@
+"""User guide code snippets for documentation."""
diff --git a/docs/source/_snippets/user_guide/experiments.py b/docs/source/_snippets/user_guide/experiments.py
new file mode 100644
index 00000000..be0c09b6
--- /dev/null
+++ b/docs/source/_snippets/user_guide/experiments.py
@@ -0,0 +1,231 @@
+"""Experiments page code snippets for documentation.
+
+This snippet file contains examples from the experiments.rst page covering
+custom objectives, built-in experiments, and benchmarks.
+"""
+
+import numpy as np
+
+
+# [start:simple_objective]
+def objective(params):
+    x = params["x"]
+    y = params["y"]
+    # Hyperactive MAXIMIZES this score
+    return -(x**2 + y**2)
+
+
+# [end:simple_objective]
+
+
+# [start:ackley_function]
+from hyperactive.opt.gfo import BayesianOptimizer
+
+
+# Ackley function (a common benchmark)
+def ackley(params):
+    x = params["x"]
+    y = params["y"]
+
+    term1 = -20 * np.exp(-0.2 * np.sqrt(0.5 * (x**2 + y**2)))
+    term2 = -np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y)))
+    result = term1 + term2 + np.e + 20
+
+    return -result  # Negate to maximize (minimize the Ackley function)
+
+
+search_space = {
+    "x": np.linspace(-5, 5, 100),
+    "y": np.linspace(-5, 5, 100),
+}
+
+optimizer = BayesianOptimizer(
+    search_space=search_space,
+    n_iter=50,
+    experiment=ackley,
+)
+best_params = optimizer.solve()
+# [end:ackley_function]
+
+
+# [start:external_simulation]
+import subprocess
+
+
+def run_simulation(params):
+    # Run an external simulation with the given parameters
+    result = subprocess.run(
+        ["./my_simulation", str(params["param1"]), str(params["param2"])],
+        capture_output=True,
+        text=True,
+    )
+    # Parse the output and return the score
+    score = float(result.stdout.strip())
+    return score
+
+
+# [end:external_simulation]
+
+
+# [start:sklearn_cv_experiment]
+from sklearn.datasets import load_iris
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import KFold
+
+from hyperactive.experiment.integrations import SklearnCvExperiment
+from hyperactive.opt.gfo import HillClimbing
+
+X, y = load_iris(return_X_y=True)
+
+experiment = SklearnCvExperiment(
+    estimator=RandomForestClassifier(random_state=42),
+    X=X,
+    y=y,
+    cv=KFold(n_splits=5, shuffle=True, random_state=42),
+    scoring=accuracy_score,  # Optional: defaults to estimator's score method
+)
+
+search_space = {
+    "n_estimators": list(range(10, 200, 10)),
+    "max_depth": list(range(1, 20)),
+    "min_samples_split": list(range(2, 10)),
+}
+
+optimizer = HillClimbing(
+    search_space=search_space,
+    n_iter=30,
+    experiment=experiment,
+)
+best_params = optimizer.solve()
+# [end:sklearn_cv_experiment]
+
+
+# [start:sktime_forecasting]
+from sktime.datasets import load_airline
+from sktime.forecasting.naive import NaiveForecaster
+
+from hyperactive.experiment.integrations import SktimeForecastingExperiment
+from hyperactive.opt.gfo import RandomSearch
+
+y = load_airline()
+
+experiment = SktimeForecastingExperiment(
+    estimator=NaiveForecaster(),
+    y=y,
+    fh=[1, 2, 3],  # Forecast horizon
+)
+
+search_space = {
+    "strategy": ["mean", "last", "drift"],
+}
+
+optimizer = RandomSearch(
+    search_space=search_space,
+    n_iter=10,
+    experiment=experiment,
+)
+best_params = optimizer.solve()
+# [end:sktime_forecasting]
+
+
+# [start:torch_experiment]
+from hyperactive.experiment.integrations import TorchExperiment
+
+experiment = TorchExperiment(
+    model_class=MyLightningModel,
+    datamodule=my_datamodule,
+    trainer_kwargs={"max_epochs": 10},
+)
+# [end:torch_experiment]
+
+
+# [start:benchmark_experiments]
+from hyperactive.experiment.bench import Ackley
+
+# Use benchmark as experiment
+ackley = Ackley(dim=2)
+
+optimizer = BayesianOptimizer(
+    search_space=ackley.search_space,
+    n_iter=50,
+    experiment=ackley,
+)
+# [end:benchmark_experiments]
+
+
+# [start:score_method]
+from hyperactive.experiment.integrations import SklearnCvExperiment
+
+experiment = SklearnCvExperiment(
+    estimator=RandomForestClassifier(),
+    X=X,
+    y=y,
+    cv=5,
+)
+
+# Evaluate specific parameters
+params = {"n_estimators": 100, "max_depth": 10}
+score, additional_info = experiment.score(params)
+
+print(f"Score: {score}")
+print(f"Additional info: {additional_info}")
+# [end:score_method]
+
+
+# [start:robust_objective]
+def robust_objective(params):
+    try:
+        score = compute_score(params)
+        return score
+    except Exception:
+        return -np.inf  # Return bad score on failure
+
+
+# [end:robust_objective]
+
+
+# --- Runnable test code below ---
+if __name__ == "__main__":
+    # Test simple objective
+    params = {"x": 0.0, "y": 0.0}
+    score = objective(params)
+    assert score == 0.0, f"Expected 0.0, got {score}"
+
+    # Test Ackley function
+    params = {"x": 0.0, "y": 0.0}
+    ackley_score = ackley(params)
+    # Ackley minimum is at (0,0) with value 0
+    assert abs(ackley_score) < 0.01, f"Expected ~0, got {ackley_score}"
+
+    # Test sklearn CV experiment
+    from sklearn.datasets import load_iris
+    from sklearn.ensemble import RandomForestClassifier
+
+    from hyperactive.experiment.integrations import SklearnCvExperiment
+    from hyperactive.opt.gfo import HillClimbing
+
+    X, y = load_iris(return_X_y=True)
+    experiment = SklearnCvExperiment(
+        estimator=RandomForestClassifier(random_state=42),
+        X=X,
+        y=y,
+        cv=3,
+    )
+
+    search_space = {
+        "n_estimators": [10, 50, 100],
+        "max_depth": [3, 5, 10],
+    }
+
+    optimizer = HillClimbing(
+        search_space=search_space,
+        n_iter=5,
+        experiment=experiment,
+        random_state=42,
+    )
+    best_params = optimizer.solve()
+    assert "n_estimators" in best_params
+    assert "max_depth" in best_params
+
+    print("Experiments snippets passed!")
diff --git a/docs/source/_snippets/user_guide/integrations.py b/docs/source/_snippets/user_guide/integrations.py
new file mode 100644
index 00000000..4d83bcf5
--- /dev/null
+++ b/docs/source/_snippets/user_guide/integrations.py
@@ -0,0 +1,277 @@
+"""Integrations page code snippets for documentation.
+
+This snippet file contains examples from the integrations.rst page covering
+sklearn, sktime, skpro, and PyTorch integrations.
+"""
+
+# [start:optcv_basic]
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+
+from hyperactive.integrations.sklearn import OptCV
+from hyperactive.opt.gfo import HillClimbing
+
+# Load data
+X, y = load_iris(return_X_y=True)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+# Define search space and optimizer
+search_space = {"kernel": ["linear", "rbf"], "C": [0.1, 1, 10, 100]}
+optimizer = HillClimbing(search_space=search_space, n_iter=20)
+
+# Create tuned estimator
+tuned_svc = OptCV(SVC(), optimizer)
+
+# Fit like any sklearn estimator
+tuned_svc.fit(X_train, y_train)
+
+# Predict
+y_pred = tuned_svc.predict(X_test)
+
+# Access results
+print(f"Best parameters: {tuned_svc.best_params_}")
+print(f"Best estimator: {tuned_svc.best_estimator_}")
+# [end:optcv_basic]
+
+
+# [start:different_optimizers]
+from hyperactive.opt import GridSearchSk as GridSearch
+from hyperactive.opt.gfo import BayesianOptimizer, GeneticAlgorithm
+from hyperactive.opt.optuna import TPEOptimizer
+
+# Grid Search (exhaustive)
+optimizer = GridSearch(search_space)
+tuned_model = OptCV(SVC(), optimizer)
+
+# Bayesian Optimization (smart sampling)
+optimizer = BayesianOptimizer(search_space=search_space, n_iter=30)
+tuned_model = OptCV(SVC(), optimizer)
+
+# Genetic Algorithm (population-based)
+optimizer = GeneticAlgorithm(search_space=search_space, n_iter=50)
+tuned_model = OptCV(SVC(), optimizer)
+
+# Optuna TPE
+optimizer = TPEOptimizer(search_space=search_space, n_iter=30)
+tuned_model = OptCV(SVC(), optimizer)
+# [end:different_optimizers]
+
+
+# [start:pipeline_integration]
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import SVC
+
+# Create pipeline
+pipe = Pipeline(
+    [
+        ("scaler", StandardScaler()),
+        ("svc", SVC()),
+    ]
+)
+
+# Search space with pipeline parameter naming
+search_space = {
+    "svc__kernel": ["linear", "rbf"],
+    "svc__C": [0.1, 1, 10],
+}
+
+optimizer = HillClimbing(search_space=search_space, n_iter=20)
+tuned_pipe = OptCV(pipe, optimizer)
+tuned_pipe.fit(X_train, y_train)
+# [end:pipeline_integration]
+
+
+# [start:forecasting_optcv]
+from sktime.datasets import load_airline
+from sktime.forecasting.naive import NaiveForecaster
+from sktime.split import ExpandingWindowSplitter, temporal_train_test_split
+
+from hyperactive.integrations.sktime import ForecastingOptCV
+from hyperactive.opt import GridSearchSk as GridSearch
+
+# Load time series data
+y = load_airline()
+y_train, y_test = temporal_train_test_split(y, test_size=12)
+
+# Define search space
+param_grid = {"strategy": ["mean", "last", "drift"]}
+
+# Create tuned forecaster
+tuned_forecaster = ForecastingOptCV(
+    NaiveForecaster(),
+    GridSearch(param_grid),
+    cv=ExpandingWindowSplitter(
+        initial_window=12,
+        step_length=3,
+        fh=range(1, 13),
+    ),
+)
+
+# Fit and predict
+tuned_forecaster.fit(y_train, fh=range(1, 13))
+y_pred = tuned_forecaster.predict()
+
+# Access results
+print(f"Best parameters: {tuned_forecaster.best_params_}")
+print(f"Best forecaster: {tuned_forecaster.best_forecaster_}")
+# [end:forecasting_optcv]
+
+
+# [start:tsc_optcv]
+from sklearn.model_selection import KFold
+from sktime.classification.dummy import DummyClassifier
+from sktime.datasets import load_unit_test
+
+from hyperactive.integrations.sktime import TSCOptCV
+from hyperactive.opt import GridSearchSk as GridSearch
+
+# Load time series classification data
+X_train, y_train = load_unit_test(
+    return_X_y=True,
+    split="TRAIN",
+    return_type="pd-multiindex",
+)
+X_test, _ = load_unit_test(
+    return_X_y=True,
+    split="TEST",
+    return_type="pd-multiindex",
+)
+
+# Define search space
+param_grid = {"strategy": ["most_frequent", "stratified"]}
+
+# Create tuned classifier
+tuned_classifier = TSCOptCV(
+    DummyClassifier(),
+    GridSearch(param_grid),
+    cv=KFold(n_splits=2, shuffle=False),
+)
+
+# Fit and predict
+tuned_classifier.fit(X_train, y_train)
+y_pred = tuned_classifier.predict(X_test)
+
+# Access results
+print(f"Best parameters: {tuned_classifier.best_params_}")
+# [end:tsc_optcv]
+
+
+# [start:skpro_experiment]
+from hyperactive.experiment.integrations import SkproProbaRegExperiment
+from hyperactive.opt.gfo import HillClimbing
+
+experiment = SkproProbaRegExperiment(
+    estimator=YourSkproEstimator(),
+    X=X,
+    y=y,
+    cv=5,
+)
+
+optimizer = HillClimbing(
+    search_space=search_space,
+    n_iter=30,
+    experiment=experiment,
+)
+best_params = optimizer.solve()
+# [end:skpro_experiment]
+
+
+# [start:pytorch_lightning]
+import lightning as L
+
+from hyperactive.experiment.integrations import TorchExperiment
+from hyperactive.opt.gfo import BayesianOptimizer
+
+
+# Define your Lightning module
+class MyModel(L.LightningModule):
+    def __init__(self, learning_rate=0.001, hidden_size=64):
+        super().__init__()
+        self.learning_rate = learning_rate
+        self.hidden_size = hidden_size
+        # ... model definition
+
+    def training_step(self, batch, batch_idx):
+        # ... training logic
+        pass
+
+    def configure_optimizers(self):
+        return torch.optim.Adam(self.parameters(), lr=self.learning_rate)
+
+
+# Create experiment
+experiment = TorchExperiment(
+    model_class=MyModel,
+    datamodule=my_datamodule,
+    trainer_kwargs={
+        "max_epochs": 10,
+        "accelerator": "auto",
+    },
+)
+
+# Define search space
+search_space = {
+    "learning_rate": [0.0001, 0.001, 0.01],
+    "hidden_size": [32, 64, 128, 256],
+}
+
+# Optimize
+optimizer = BayesianOptimizer(
+    search_space=search_space,
+    n_iter=20,
+    experiment=experiment,
+)
+best_params = optimizer.solve()
+# [end:pytorch_lightning]
+
+
+# --- Runnable test code below ---
+if __name__ == "__main__":
+    from sklearn.datasets import load_iris
+    from sklearn.model_selection import train_test_split
+    from sklearn.pipeline import Pipeline
+    from sklearn.preprocessing import StandardScaler
+    from sklearn.svm import SVC
+
+    from hyperactive.integrations.sklearn import OptCV
+    from hyperactive.opt.gfo import HillClimbing
+
+    # Test OptCV basic usage
+    X, y = load_iris(return_X_y=True)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.2, random_state=42
+    )
+
+    search_space = {"kernel": ["linear", "rbf"], "C": [0.1, 1, 10]}
+    optimizer = HillClimbing(search_space=search_space, n_iter=10)
+    tuned_svc = OptCV(SVC(), optimizer)
+    tuned_svc.fit(X_train, y_train)
+    y_pred = tuned_svc.predict(X_test)
+
+    assert hasattr(tuned_svc, "best_params_")
+    assert hasattr(tuned_svc, "best_estimator_")
+    assert "kernel" in tuned_svc.best_params_
+    assert "C" in tuned_svc.best_params_
+
+    # Test pipeline integration
+    pipe = Pipeline(
+        [
+            ("scaler", StandardScaler()),
+            ("svc", SVC()),
+        ]
+    )
+
+    search_space = {
+        "svc__kernel": ["linear", "rbf"],
+        "svc__C": [0.1, 1],
+    }
+
+    optimizer = HillClimbing(search_space=search_space, n_iter=5)
+    tuned_pipe = OptCV(pipe, optimizer)
+    tuned_pipe.fit(X_train, y_train)
+
+    assert hasattr(tuned_pipe, "best_params_")
+
+    print("Integrations snippets passed!")
diff --git a/docs/source/_snippets/user_guide/introduction.py b/docs/source/_snippets/user_guide/introduction.py
new file mode 100644
index 00000000..17b2376b
--- /dev/null
+++ b/docs/source/_snippets/user_guide/introduction.py
@@ -0,0 +1,124 @@
+"""Introduction page code snippets for documentation.
+
+This snippet file contains examples from the introduction.rst page.
+Some snippets are illustrative (showing patterns) while others are runnable.
+"""
+
+import numpy as np
+
+# Define placeholders for illustrative code
+# These allow the file to be imported without errors
+X_train = np.array([[1, 2], [3, 4], [5, 6]])
+y_train = np.array([0, 1, 0])
+X = X_train
+y = y_train
+
+
+# [start:simple_objective]
+def objective(params):
+    x = params["x"]
+    y = params["y"]
+    # Return a score to maximize
+    return -(x**2 + y**2)
+
+
+# [end:simple_objective]
+
+
+# [start:sklearn_experiment_intro]
+from sklearn.ensemble import RandomForestClassifier
+
+from hyperactive.experiment.integrations import SklearnCvExperiment
+
+experiment = SklearnCvExperiment(
+    estimator=RandomForestClassifier(),
+    X=X_train,
+    y=y_train,
+    cv=5,
+)
+# [end:sklearn_experiment_intro]
+
+
+# [start:optimizer_imports]
+# [end:optimizer_imports]
+# [start:search_space_definition]
+import numpy as np
+
+from hyperactive.opt.gfo import (
+    HillClimbing,  # Local search
+)
+
+search_space = {
+    # Discrete integer values
+    "n_estimators": list(range(10, 200, 10)),
+    # Continuous values (discretized)
+    "learning_rate": np.logspace(-4, 0, 20),
+    # Categorical values
+    "kernel": ["linear", "rbf", "poly"],
+}
+# [end:search_space_definition]
+
+
+# [start:workflow_experiment_options]
+# Option A: Custom function
+def my_objective(params):
+    # Your evaluation logic here
+    return score
+
+
+# Option B: Built-in sklearn experiment
+from hyperactive.experiment.integrations import SklearnCvExperiment
+
+experiment = SklearnCvExperiment(
+    estimator=YourEstimator(),
+    X=X,
+    y=y,
+    cv=5,
+)
+# [end:workflow_experiment_options]
+
+
+# [start:workflow_search_space]
+search_space = {
+    "param1": [1, 2, 3, 4, 5],
+    "param2": np.linspace(0.1, 1.0, 10),
+    "param3": ["option_a", "option_b"],
+}
+# [end:workflow_search_space]
+
+
+# [start:workflow_optimizer]
+
+optimizer = HillClimbing(
+    search_space=search_space,
+    n_iter=100,  # Number of iterations
+    experiment=experiment,
+    random_state=42,  # For reproducibility
+)
+# [end:workflow_optimizer]
+
+
+# [start:workflow_solve]
+best_params = optimizer.solve()
+print(f"Best parameters: {best_params}")
+# [end:workflow_solve]
+
+
+# [start:warm_starting]
+warm_start = [
+    {"n_estimators": 100, "max_depth": 10},  # Start from known good point
+]
+
+optimizer = HillClimbing(
+    search_space=search_space,
+    n_iter=50,
+    experiment=experiment,
+    initialize={"warm_start": warm_start},
+)
+# [end:warm_starting]
+
+
+if __name__ == "__main__":
+    # The actual test code runs here
+    print("Introduction snippet file is importable!")
+    print("Full integration test runs in test_doc_snippets.py")
diff --git a/docs/source/_snippets/user_guide/optimizers.py b/docs/source/_snippets/user_guide/optimizers.py
new file mode 100644
index 00000000..181da4e5
--- /dev/null
+++ b/docs/source/_snippets/user_guide/optimizers.py
@@ -0,0 +1,324 @@
+"""Optimizers page code snippets for documentation.
+
+This snippet file contains examples from the optimizers.rst page covering
+all optimizer categories and configurations.
+"""
+
+import numpy as np
+
+# Define common test fixtures
+search_space = {
+    "x": np.arange(-5, 5, 0.5),
+    "y": np.arange(-5, 5, 0.5),
+}
+
+
+def objective(params):
+    x = params["x"]
+    y = params["y"]
+    return -(x**2 + y**2)
+
+
+# ============================================================================
+# Local Search Optimizers
+# ============================================================================
+
+# [start:hill_climbing]
+from hyperactive.opt.gfo import HillClimbing
+
+optimizer = HillClimbing(
+    search_space=search_space,
+    n_iter=100,
+    experiment=objective,
+)
+# [end:hill_climbing]
+
+
+# [start:simulated_annealing]
+from hyperactive.opt.gfo import SimulatedAnnealing
+
+optimizer = SimulatedAnnealing(
+    search_space=search_space,
+    n_iter=100,
+    experiment=objective,
+)
+# [end:simulated_annealing]
+
+
+# [start:repulsing_hill_climbing]
+from hyperactive.opt.gfo import RepulsingHillClimbing
+
+optimizer = RepulsingHillClimbing(
+    search_space=search_space,
+    n_iter=100,
+    experiment=objective,
+)
+# [end:repulsing_hill_climbing]
+
+
+# [start:stochastic_hill_climbing]
+from hyperactive.opt.gfo import StochasticHillClimbing
+
+optimizer = StochasticHillClimbing(
+    search_space=search_space,
+    n_iter=100,
+    experiment=objective,
+    p_accept=0.3,  # Probability of accepting worse solutions
+)
+# [end:stochastic_hill_climbing]
+
+
+# [start:downhill_simplex]
+from hyperactive.opt.gfo import DownhillSimplexOptimizer
+
+optimizer = DownhillSimplexOptimizer(
+    search_space=search_space,
+    n_iter=100,
+    experiment=objective,
+)
+# [end:downhill_simplex]
+
+
+# ============================================================================
+# Global Search Optimizers
+# ============================================================================
+
+# [start:random_search]
+from hyperactive.opt.gfo import RandomSearch
+
+optimizer = RandomSearch(
+    search_space=search_space,
+    n_iter=100,
+    experiment=objective,
+)
+# [end:random_search]
+
+
+# [start:grid_search]
+from hyperactive.opt.gfo import GridSearch
+
+optimizer = GridSearch(
+    search_space=search_space,
+    experiment=objective,
+)
+# [end:grid_search]
+
+
+# [start:random_restart_hill_climbing]
+from hyperactive.opt.gfo import RandomRestartHillClimbing
+
+optimizer = RandomRestartHillClimbing(
+    search_space=search_space,
+    n_iter=100,
+    experiment=objective,
+)
+# [end:random_restart_hill_climbing]
+
+
+# [start:powells_pattern]
+# [end:powells_pattern]
+
+
+# ============================================================================
+# Population Methods
+# ============================================================================
+
+# [start:particle_swarm]
+from hyperactive.opt.gfo import ParticleSwarmOptimizer
+
+optimizer = ParticleSwarmOptimizer(
+    search_space=search_space,
+    n_iter=100,
+    experiment=objective,
+)
+# [end:particle_swarm]
+
+
+# [start:genetic_algorithm]
+from hyperactive.opt.gfo import GeneticAlgorithm
+
+optimizer = GeneticAlgorithm(
+    search_space=search_space,
+    n_iter=100,
+    experiment=objective,
+)
+# [end:genetic_algorithm]
+
+
+# [start:evolution_strategy]
+# [end:evolution_strategy]
+
+
+# [start:differential_evolution]
+# [end:differential_evolution]
+
+
+# [start:parallel_tempering]
+# [end:parallel_tempering]
+
+
+# [start:spiral_optimization]
+# [end:spiral_optimization]
+
+
+# ============================================================================
+# Sequential Model-Based (Bayesian)
+# ============================================================================
+
+# [start:bayesian_optimizer]
+from hyperactive.opt.gfo import BayesianOptimizer
+
+optimizer = BayesianOptimizer(
+    search_space=search_space,
+    n_iter=50,
+    experiment=objective,
+)
+# [end:bayesian_optimizer]
+
+
+# [start:tpe]
+# [end:tpe]
+
+
+# [start:forest_optimizer]
+# [end:forest_optimizer]
+
+
+# [start:lipschitz_direct]
+# [end:lipschitz_direct]
+
+
+# ============================================================================
+# Optuna Backend
+# ============================================================================
+
+# [start:optuna_imports]
+from hyperactive.opt.optuna import (
+    TPEOptimizer,  # Tree-Parzen Estimators
+)
+
+# [end:optuna_imports]
+
+
+# [start:optuna_tpe]
+
+optimizer = TPEOptimizer(
+    search_space=search_space,
+    n_iter=50,
+    experiment=objective,
+)
+# [end:optuna_tpe]
+
+
+# ============================================================================
+# Configuration Examples
+# ============================================================================
+
+# [start:common_parameters]
+optimizer = SomeOptimizer(
+    search_space=search_space,  # Required: parameter ranges
+    n_iter=100,  # Required: number of iterations
+    experiment=objective,  # Required: objective function
+    random_state=42,  # Optional: for reproducibility
+    initialize={  # Optional: initialization settings
+        "warm_start": [...],  # Starting points
+        "grid": 4,  # Grid initialization points
+        "random": 2,  # Random initialization points
+        "vertices": 4,  # Vertex initialization points
+    },
+)
+# [end:common_parameters]
+
+
+# [start:warm_start_example]
+# Start from known good points
+optimizer = HillClimbing(
+    search_space=search_space,
+    n_iter=50,
+    experiment=objective,
+    initialize={
+        "warm_start": [
+            {"param1": 10, "param2": 0.5},
+            {"param1": 20, "param2": 0.3},
+        ]
+    },
+)
+# [end:warm_start_example]
+
+
+# [start:initialization_strategies]
+# Mix of initialization strategies
+optimizer = ParticleSwarmOptimizer(
+    search_space=search_space,
+    n_iter=100,
+    experiment=objective,
+    initialize={
+        "grid": 4,  # 4 points on a grid
+        "random": 6,  # 6 random points
+        "vertices": 4,  # 4 corner points
+    },
+)
+# [end:initialization_strategies]
+
+
+# [start:simulated_annealing_config]
+from hyperactive.opt.gfo import SimulatedAnnealing
+
+optimizer = SimulatedAnnealing(
+    search_space=search_space,
+    n_iter=100,
+    experiment=objective,
+    # Algorithm-specific parameters
+    # (check API reference for available options)
+)
+# [end:simulated_annealing_config]
+
+
+# --- Runnable test code below ---
+if __name__ == "__main__":
+    from hyperactive.opt.gfo import (
+        BayesianOptimizer,
+        GeneticAlgorithm,
+        HillClimbing,
+        ParticleSwarmOptimizer,
+        RandomSearch,
+        SimulatedAnnealing,
+    )
+
+    search_space = {
+        "x": np.arange(-5, 5, 0.5),
+        "y": np.arange(-5, 5, 0.5),
+    }
+
+    def objective(params):
+        x = params["x"]
+        y = params["y"]
+        return -(x**2 + y**2)
+
+    # Test a few optimizers
+    optimizers_to_test = [
+        ("HillClimbing", HillClimbing),
+        ("SimulatedAnnealing", SimulatedAnnealing),
+        ("RandomSearch", RandomSearch),
+        ("BayesianOptimizer", BayesianOptimizer),
+    ]
+
+    for name, OptimizerClass in optimizers_to_test:
+        if name == "BayesianOptimizer":
+            optimizer = OptimizerClass(
+                search_space=search_space,
+                n_iter=10,
+                experiment=objective,
+            )
+        else:
+            optimizer = OptimizerClass(
+                search_space=search_space,
+                n_iter=20,
+                experiment=objective,
+            )
+        best_params = optimizer.solve()
+        assert "x" in best_params
+        assert "y" in best_params
+        print(f"{name} passed!")
+
+    print("All optimizer snippets passed!")
diff --git a/docs/source/_static/css/custom.css b/docs/source/_static/css/custom.css
new file mode 100644
index 00000000..89cf8ca3
--- /dev/null
+++ b/docs/source/_static/css/custom.css
@@ -0,0 +1,1300 @@
+/* Custom CSS for Hyperactive documentation */
+/* Purple/Violet theme matching the Hyperactive logo */
+
+/* ============================================
+   Google Font Import for Hero Title
+   ============================================ */
+@import url('https://fonts.googleapis.com/css2?family=Raleway:wght@300;400;600;700&display=swap');
+
+/* ============================================
+   PyData Theme Color Overrides (purple theme)
+   ============================================ */
+html[data-theme="light"] {
+    --pst-color-primary: #5D5D7A;
+    --pst-color-primary-bg: #f0f0f8;
+    --pst-color-secondary: #7070A0;
+    --pst-color-accent: #8080B0;
+    --pst-color-info: #7070A0;
+    --pst-color-link: #5D5D7A;
+    --pst-color-link-hover: #4A4A65;
+    --pst-color-target: rgba(125, 125, 170, 0.2);
+}
+
+html[data-theme="dark"] {
+    --pst-color-primary: #9090C0;
+    --pst-color-primary-bg: #2a2a3a;
+    --pst-color-secondary: #A0A0D0;
+    --pst-color-accent: #B0B0E0;
+    --pst-color-info: #9090C0;
+    --pst-color-link: #A0A0D0;
+    --pst-color-link-hover: #B0B0E0;
+    --pst-color-target: rgba(160, 160, 208, 0.2);
+    --separator-color: #3d3d4d;
+}
+
+/* ============================================
+   CSS Variables for consistent theming
+   ============================================ */
+:root {
+    /* Purple/violet colors from the Hyperactive logo */
+    --hyperactive-primary: #5D5D7A;
+    --hyperactive-secondary: #7070A0;
+    --hyperactive-accent: #8080B0;
+    --hyperactive-dark: #4A4A65;
+    --hyperactive-light: #f8f9fa;
+    --hyperactive-gradient: linear-gradient(135deg, #3D3D55 0%, #5858A0 100%);
+    --hyperactive-gradient-dark: linear-gradient(135deg, #2D2D45 0%, #484890 100%);
+    --card-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
+    --card-shadow-hover: 0 20px 25px -5px rgba(0, 0, 0, 0.1), 0 10px 10px -5px rgba(0, 0, 0, 0.04);
+    /* Neutral separator color */
+    --separator-color: #dee2e6;
+
+    /* Backend brand colors - from official logos */
+    --gfo-light: #80b4ff;
+    --gfo-dark: #000054;
+    --gfo-grey: #555555;
+    --optuna-dark: #123692;
+    --optuna-light: #3b82c4;
+    --sklearn-orange: #F7931E;
+    --sklearn-blue: #29ABE2;
+
+    /* Integration brand colors */
+    --sktime-blue: #0075b7;
+    --sktime-teal: #00a890;
+    --skpro-blue: #0076b7;
+    --skpro-teal: #01ab90;
+    --pytorch-orange: #ee4c2c;
+    --pytorch-yellow: #eaa700;
+}
+
+/* ============================================
+   Top Navbar Styling
+   ============================================ */
+.bd-header.navbar {
+    padding-top: 0 !important;
+    padding-bottom: 0 !important;
+    min-height: auto !important;
+}
+
+.bd-header .navbar-header-items {
+    flex-wrap: nowrap !important;
+}
+
+.bd-header .navbar-header-items__center {
+    flex-wrap: nowrap !important;
+}
+
+.bd-header .navbar-header-items__start {
+    padding-top: 0 !important;
+    padding-bottom: 0 !important;
+}
+
+.navbar-brand {
+    padding: 0.25rem 0 !important;
+    margin-right: 0.75rem !important;
+    flex-shrink: 0 !important;
+}
+
+.navbar-brand .logo__image {
+    height: 36px !important;
+    width: auto !important;
+    max-height: 36px !important;
+    display: inline-block !important;
+    visibility: visible !important;
+    opacity: 1 !important;
+}
+
+.navbar-brand p,
+.navbar-brand .logo__title {
+    display: none !important;
+}
+
+.bd-navbar .navbar-nav {
+    gap: 0.1rem;
+    flex-wrap: nowrap !important;
+}
+
+.bd-navbar .nav-link {
+    padding: 0.4rem 0.5rem !important;
+    white-space: nowrap;
+    font-size: 0.85rem;
+}
+
+.bd-navbar .dropdown-toggle {
+    padding: 0.4rem 0.5rem !important;
+    font-size: 0.85rem;
+}
+
+/* ============================================
+   Right Sidebar - On This Page spacing
+   ============================================ */
+.bd-toc-nav.page-toc {
+    margin-top: 1.25rem;
+}
+
+.pst-page-navigation-heading {
+    margin-bottom: 0.5rem;
+}
+
+/* ============================================
+   Hero Section
+   ============================================ */
+.hero-section {
+    background: var(--hyperactive-gradient);
+    padding: 3rem 2rem;
+    margin: -1rem -1rem 2rem -1rem;
+    border-radius: 0 0 20px 20px;
+    text-align: center;
+    position: relative;
+    overflow: hidden;
+}
+
+.hero-section::before {
+    content: '';
+    position: absolute;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background:
+        radial-gradient(circle at 20% 80%, rgba(255, 255, 255, 0.1) 0%, transparent 50%),
+        radial-gradient(circle at 80% 20%, rgba(255, 255, 255, 0.1) 0%, transparent 50%);
+    pointer-events: none;
+}
+
+.hero-content {
+    position: relative;
+    z-index: 1;
+}
+
+.hero-title {
+    font-family: 'Raleway', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+    font-size: 3.5rem;
+    font-weight: 300;
+    color: #ffffff;
+    margin: 0 0 3.5rem 0;
+    letter-spacing: 0.25em;
+    text-transform: uppercase;
+    text-shadow: 0 2px 8px rgba(0, 0, 0, 0.25);
+}
+
+.hero-tagline {
+    font-size: 1.25rem;
+    font-weight: 400;
+    color: rgba(255, 255, 255, 0.9);
+    margin: 0 0 0.5rem 0;
+    letter-spacing: 0.05em;
+    font-style: italic;
+}
+
+/* ============================================
+   Back to Top Link (in sidebar)
+   ============================================ */
+.back-to-top-sidebar {
+    display: none;
+    margin-top: 1.5rem;
+    padding-top: 1rem;
+    border-top: 1px solid var(--pst-color-border);
+}
+
+.back-to-top-sidebar.visible {
+    display: block;
+}
+
+.back-to-top-sidebar a {
+    display: inline-flex;
+    align-items: center;
+    gap: 0.5rem;
+    color: var(--hyperactive-primary);
+    text-decoration: none;
+    font-size: 0.85rem;
+    font-weight: 500;
+    transition: color 0.2s ease;
+}
+
+.back-to-top-sidebar a:hover {
+    color: var(--hyperactive-dark);
+}
+
+html[data-theme="dark"] .back-to-top-sidebar a {
+    color: var(--hyperactive-secondary);
+}
+
+html[data-theme="dark"] .back-to-top-sidebar a:hover {
+    color: var(--hyperactive-accent);
+}
+
+.hero-subtitle {
+    font-size: 1.1rem;
+    color: rgba(255, 255, 255, 0.9);
+    margin: 0;
+}
+
+/* ============================================
+   Stats Strip (below hero)
+   ============================================ */
+.stats-strip {
+    display: flex;
+    justify-content: center;
+    align-items: stretch;
+    gap: 0;
+    margin: 0 0 2rem 0;
+    padding: 0;
+    border-radius: 12px;
+    overflow: hidden;
+    box-shadow: var(--card-shadow);
+    border: 1px solid var(--pst-color-border);
+    background: var(--pst-color-surface);
+}
+
+.stat-item {
+    flex: 1;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    justify-content: center;
+    padding: 1.25rem 1rem;
+    text-align: center;
+    position: relative;
+    transition: background 0.2s ease;
+}
+
+.stat-item:not(:last-child)::after {
+    content: '';
+    position: absolute;
+    right: 0;
+    top: 20%;
+    height: 60%;
+    width: 1px;
+    background: var(--pst-color-border);
+}
+
+.stat-item:hover {
+    background: var(--pst-color-background);
+}
+
+.stat-value {
+    font-size: 1.75rem;
+    font-weight: 700;
+    color: var(--hyperactive-primary);
+    line-height: 1.2;
+    margin-bottom: 0.25rem;
+}
+
+html[data-theme="dark"] .stat-value {
+    color: var(--hyperactive-secondary);
+}
+
+.stat-label {
+    font-size: 0.8rem;
+    font-weight: 500;
+    color: var(--pst-color-text-muted);
+    text-transform: uppercase;
+    letter-spacing: 0.05em;
+}
+
+.stat-icon {
+    font-size: 1.25rem;
+    margin-bottom: 0.5rem;
+    opacity: 0.7;
+}
+
+/* Responsive: stack on mobile */
+@media (max-width: 640px) {
+    .stats-strip {
+        flex-wrap: wrap;
+    }
+
+    .stat-item {
+        flex: 1 1 50%;
+        padding: 1rem 0.75rem;
+    }
+
+    .stat-item:not(:last-child)::after {
+        display: none;
+    }
+
+    .stat-item:nth-child(1),
+    .stat-item:nth-child(2) {
+        border-bottom: 1px solid var(--pst-color-border);
+    }
+
+    .stat-item:nth-child(odd) {
+        border-right: 1px solid var(--pst-color-border);
+    }
+
+    .stat-value {
+        font-size: 1.5rem;
+    }
+}
+
+/* ============================================
+   Badge Banner (replaces Maturity Banner)
+   ============================================ */
+.badge-banner {
+    text-align: center;
+    padding: 1rem 1rem 1.5rem;
+    margin: 1rem 0;
+}
+
+.badge-items {
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    gap: 0.75rem;
+    flex-wrap: wrap;
+}
+
+.badge-items a {
+    display: inline-block;
+    line-height: 0;
+}
+
+.badge-items img {
+    height: 28px;
+    vertical-align: middle;
+    border-radius: 0 !important;
+    image-rendering: crisp-edges;
+}
+
+/* Legacy Maturity Banner (kept for backwards compatibility) */
+.maturity-banner {
+    background: linear-gradient(90deg, #f8f9fa 0%, #e9ecef 50%, #f8f9fa 100%);
+    border-radius: 12px;
+    padding: 1rem 1.5rem;
+    margin: 1.5rem 0;
+    border: 1px solid #dee2e6;
+}
+
+html[data-theme="dark"] .maturity-banner {
+    background: linear-gradient(90deg, #2d2d3a 0%, #252530 50%, #2d2d3a 100%);
+    border-color: #3d3d4d;
+}
+
+.maturity-items {
+    display: flex;
+    justify-content: center;
+    gap: 2rem;
+    flex-wrap: wrap;
+}
+
+.maturity-item {
+    display: flex;
+    align-items: center;
+    gap: 0.5rem;
+    color: #495057;
+}
+
+html[data-theme="dark"] .maturity-item {
+    color: #adb5bd;
+}
+
+.maturity-icon {
+    font-size: 1.25rem;
+}
+
+.maturity-text {
+    font-size: 0.95rem;
+}
+
+/* ============================================
+   Feature Cards
+   ============================================ */
+.feature-card {
+    border: 1px solid #e9ecef !important;
+    border-left: 3px solid var(--hyperactive-primary) !important;
+    box-shadow: var(--card-shadow);
+    transition: all 0.2s ease;
+    border-radius: 8px !important;
+    overflow: hidden;
+}
+
+html[data-theme="dark"] .feature-card {
+    border-color: #3d3d4d !important;
+    border-left-color: var(--hyperactive-secondary) !important;
+}
+
+.feature-card:hover {
+    box-shadow: var(--card-shadow-hover);
+    border-left-color: var(--hyperactive-secondary) !important;
+}
+
+html[data-theme="dark"] .feature-card:hover {
+    border-left-color: var(--hyperactive-accent) !important;
+}
+
+.feature-card .sd-card-body {
+    padding: 1.25rem;
+}
+
+.feature-card .sd-card-footer {
+    font-size: 0.85rem;
+    color: var(--hyperactive-dark);
+    padding: 0;
+    background: #f0f1f3;
+    border-top: 1px solid #e0e2e6;
+}
+
+.feature-card .sd-card-footer p {
+    margin: 0;
+}
+
+.feature-card .sd-card-footer a {
+    display: flex;
+    align-items: center;
+    padding: 0.75rem 1.25rem;
+    color: var(--hyperactive-dark);
+    text-decoration: none;
+    transition: all 0.2s ease;
+    position: relative;
+}
+
+.feature-card .sd-card-footer a::after {
+    content: "→";
+    margin-left: auto;
+    padding-left: 1rem;
+    opacity: 0;
+    transform: translateX(-8px);
+    transition: all 0.2s ease;
+}
+
+.feature-card .sd-card-footer a:hover {
+    background: #e4e6ea;
+}
+
+.feature-card .sd-card-footer a:hover::after {
+    opacity: 0.6;
+    transform: translateX(0);
+}
+
+html[data-theme="dark"] .feature-card .sd-card-footer {
+    background: #2a2a3a;
+    border-top-color: #3d3d4d;
+    color: #c8c8d8;
+}
+
+html[data-theme="dark"] .feature-card .sd-card-footer a {
+    color: #c8c8d8;
+}
+
+html[data-theme="dark"] .feature-card .sd-card-footer a:hover {
+    background: #3a3a4a;
+}
+
+/* ============================================
+   Algorithm Cards
+   ============================================ */
+.algo-card {
+    border-left: 4px solid var(--hyperactive-primary) !important;
+    background: linear-gradient(135deg, #ffffff 0%, #f8f9fa 100%);
+    transition: all 0.2s ease;
+}
+
+html[data-theme="dark"] .algo-card {
+    background: linear-gradient(135deg, #2d2d3a 0%, #252530 100%);
+}
+
+.algo-card:hover {
+    border-left-color: var(--hyperactive-secondary) !important;
+    transform: translateX(4px);
+}
+
+.algo-card ul {
+    margin: 0;
+    padding-left: 1.2rem;
+}
+
+.algo-card li {
+    margin-bottom: 0.3rem;
+    font-size: 0.9rem;
+}
+
+.optuna-card {
+    border-left-color: var(--hyperactive-accent) !important;
+}
+
+.optuna-card:hover {
+    border-left-color: var(--hyperactive-primary) !important;
+}
+
+/* ============================================
+   Backend Cards (GFO, Optuna, sklearn)
+   ============================================ */
+.backend-card {
+    border-radius: 8px !important;
+    overflow: hidden;
+    box-shadow: var(--card-shadow);
+    border: none !important;
+    position: relative;
+    background: var(--pst-color-surface);
+}
+
+/* Side accent bar using pseudo-element for multi-color support */
+.backend-card::before {
+    content: '';
+    position: absolute;
+    left: 0;
+    top: 0;
+    bottom: 0;
+    width: 6px;
+    border-radius: 8px 0 0 8px;
+}
+
+.backend-card .sd-card-header {
+    padding: 1.25rem 1.5rem 0.75rem;
+    border-bottom: none;
+    background: transparent;
+}
+
+.backend-card .sd-card-header .sd-card-text {
+    font-size: 1.25rem;
+    font-weight: 600;
+    margin: 0;
+}
+
+.backend-card .sd-card-header a {
+    text-decoration: none;
+    transition: opacity 0.2s ease;
+}
+
+.backend-card .sd-card-header a:hover {
+    text-decoration: underline;
+}
+
+.backend-card .sd-card-body {
+    padding: 0.5rem 1.5rem 1rem;
+    font-size: 0.95rem;
+    line-height: 1.6;
+}
+
+.backend-card .sd-card-body ul {
+    margin: 0.75rem 0 0 0;
+    padding-left: 1.25rem;
+}
+
+.backend-card .sd-card-body li {
+    margin-bottom: 0.4rem;
+}
+
+.backend-card .sd-card-footer {
+    padding: 0;
+    border-top: 1px solid var(--pst-color-border);
+    background: transparent;
+    transition: background 0.2s ease;
+}
+
+.backend-card .sd-card-footer a {
+    display: flex;
+    align-items: center;
+    padding: 0.875rem 1.5rem;
+    text-decoration: none;
+    font-weight: 500;
+    transition: all 0.2s ease;
+    position: relative;
+}
+
+.backend-card .sd-card-footer a::after {
+    content: "→";
+    margin-left: auto;
+    padding-left: 1rem;
+    opacity: 0;
+    transform: translateX(-8px);
+    transition: all 0.2s ease;
+}
+
+.backend-card .sd-card-footer:hover {
+    background: var(--pst-color-background);
+}
+
+.backend-card .sd-card-footer a:hover::after {
+    opacity: 0.6;
+    transform: translateX(0);
+}
+
+/* GFO: Tri-color (dark navy | light blue | grey) */
+.backend-card-gfo::before {
+    background: linear-gradient(to bottom,
+        var(--gfo-dark) 0%, var(--gfo-dark) 33%,
+        var(--gfo-light) 33%, var(--gfo-light) 66%,
+        var(--gfo-grey) 66%, var(--gfo-grey) 100%);
+}
+
+/* Optuna: Vertical gradient (dark blue → light blue) */
+.backend-card-optuna::before {
+    background: linear-gradient(to bottom, var(--optuna-dark) 0%, var(--optuna-light) 100%);
+}
+
+/* sklearn: 50/50 split (orange | cyan) */
+.backend-card-sklearn::before {
+    background: linear-gradient(to bottom, var(--sklearn-orange) 50%, var(--sklearn-blue) 50%);
+}
+
+/* ============================================
+   Integration Cards (horizontal, two-part)
+   ============================================ */
+.integration-grid {
+    display: grid;
+    grid-template-columns: repeat(2, 1fr);
+    gap: 1.25rem;
+    margin: 1.5rem 0;
+}
+
+@media (max-width: 768px) {
+    .integration-grid {
+        grid-template-columns: 1fr;
+    }
+}
+
+.integration-card {
+    background: var(--pst-color-surface);
+    border-radius: 8px;
+    display: flex;
+    box-shadow: var(--card-shadow);
+    position: relative;
+    overflow: hidden;
+}
+
+.integration-card::before {
+    content: '';
+    position: absolute;
+    left: 0;
+    top: 0;
+    bottom: 0;
+    width: 5px;
+}
+
+.integration-left {
+    padding: 1.25rem 1.5rem;
+    display: flex;
+    align-items: center;
+    min-width: 140px;
+}
+
+.integration-left a {
+    font-size: 1.1rem;
+    font-weight: 600;
+    text-decoration: none;
+    transition: opacity 0.2s ease;
+}
+
+.integration-left a:hover {
+    text-decoration: underline;
+}
+
+.integration-right {
+    flex: 1;
+    display: flex;
+    align-items: center;
+    border-left: 1px solid var(--pst-color-border);
+    position: relative;
+    transition: background 0.2s ease;
+}
+
+.integration-right a {
+    display: flex;
+    align-items: center;
+    padding: 1.25rem 1.5rem;
+    width: 100%;
+    height: 100%;
+    text-decoration: none;
+    color: inherit;
+    transition: all 0.2s ease;
+}
+
+.integration-right a::after {
+    content: "→";
+    margin-left: auto;
+    padding-left: 1rem;
+    opacity: 0;
+    transform: translateX(-8px);
+    transition: all 0.2s ease;
+}
+
+.integration-right:hover {
+    background: var(--pst-color-background);
+}
+
+.integration-right:hover a::after {
+    opacity: 0.6;
+    transform: translateX(0);
+}
+
+.integration-desc {
+    font-size: 0.95rem;
+    line-height: 1.4;
+}
+
+/* Integration card colors */
+.integration-card.sklearn::before {
+    background: linear-gradient(to bottom, var(--sklearn-orange) 50%, var(--sklearn-blue) 50%);
+}
+
+.integration-card.sktime::before {
+    background: linear-gradient(to bottom, var(--sktime-blue) 0%, var(--sktime-teal) 100%);
+}
+
+.integration-card.skpro::before {
+    background: linear-gradient(to bottom, var(--skpro-blue) 0%, var(--skpro-teal) 100%);
+}
+
+.integration-card.pytorch::before {
+    background: linear-gradient(to bottom, var(--pytorch-orange) 50%, var(--pytorch-yellow) 50%);
+}
+
+/* ============================================
+   Navigation Cards
+   ============================================ */
+/* ============================================
+   Contents Navigation Cards (horizontal style)
+   ============================================ */
+.contents-grid {
+    display: grid;
+    grid-template-columns: repeat(3, 1fr);
+    gap: 1rem;
+    margin: 1.5rem 0;
+}
+
+@media (max-width: 992px) {
+    .contents-grid {
+        grid-template-columns: repeat(2, 1fr);
+    }
+}
+
+@media (max-width: 576px) {
+    .contents-grid {
+        grid-template-columns: 1fr;
+    }
+}
+
+.contents-card {
+    background: var(--pst-color-surface);
+    border-radius: 8px;
+    display: flex;
+    align-items: stretch;
+    box-shadow: var(--card-shadow);
+    position: relative;
+    overflow: hidden;
+    text-decoration: none;
+    color: inherit;
+    transition: box-shadow 0.2s ease;
+}
+
+.contents-card::before {
+    content: '';
+    position: absolute;
+    left: 0;
+    top: 0;
+    bottom: 0;
+    width: 4px;
+    background: linear-gradient(to bottom, var(--hyperactive-primary) 0%, var(--hyperactive-secondary) 100%);
+}
+
+.contents-card:hover {
+    box-shadow: var(--card-shadow-hover);
+}
+
+.contents-card-inner {
+    display: flex;
+    align-items: center;
+    width: 100%;
+    padding: 1rem 1.25rem 1rem 1.5rem;
+    transition: all 0.2s ease;
+}
+
+.contents-card:hover .contents-card-inner {
+    background: var(--pst-color-background);
+}
+
+.contents-card-text {
+    flex: 1;
+}
+
+.contents-card-title {
+    font-size: 1rem;
+    font-weight: 600;
+    margin: 0 0 0.25rem 0;
+}
+
+.contents-card-desc {
+    font-size: 0.85rem;
+    opacity: 0.7;
+    margin: 0;
+    line-height: 1.3;
+}
+
+.contents-card-arrow {
+    opacity: 0;
+    transform: translateX(-8px);
+    transition: all 0.2s ease;
+    margin-left: 1rem;
+    font-size: 1rem;
+}
+
+.contents-card:hover .contents-card-arrow {
+    opacity: 0.6;
+    transform: translateX(0);
+}
+
+/* ============================================
+   Visualization Section
+   ============================================ */
+.visualization-section {
+    text-align: center;
+    padding: 2rem 0;
+}
+
+.visualization-section h2 {
+    color: var(--hyperactive-primary);
+    margin-bottom: 0.5rem;
+}
+
+.visualization-section p {
+    color: #6c757d;
+    margin-bottom: 1.5rem;
+}
+
+.optimization-gif {
+    max-width: 100%;
+    max-height: 400px;
+    border-radius: 12px;
+    box-shadow: var(--card-shadow-hover);
+    border: 3px solid var(--hyperactive-primary);
+}
+
+/* ============================================
+   Sponsor Section
+   ============================================ */
+.sponsor-section {
+    text-align: center;
+    padding: 1.5rem 0;
+}
+
+.sponsor-section img {
+    transition: transform 0.2s ease;
+}
+
+.sponsor-section img:hover {
+    transform: scale(1.05);
+}
+
+/* ============================================
+   Code Blocks Enhancement
+   ============================================ */
+.highlight {
+    border-radius: 8px;
+    overflow: hidden;
+}
+
+.highlight pre {
+    border-radius: 8px;
+    padding: 1rem;
+}
+
+/* ============================================
+   Tab Set Styling
+   ============================================ */
+.sd-tab-set {
+    border-radius: 12px;
+    overflow: hidden;
+    box-shadow: var(--card-shadow);
+}
+
+.sd-tab-label {
+    font-weight: 500;
+    transition: all 0.2s ease;
+}
+
+.sd-tab-label:hover {
+    background-color: rgba(93, 93, 122, 0.1);
+}
+
+input[name^="sd-tab-set"]:checked + label {
+    border-bottom-color: var(--hyperactive-primary) !important;
+    color: var(--hyperactive-primary);
+}
+
+/* ============================================
+   Section Separators - Full Width Horizontal Lines
+   ============================================ */
+hr {
+    border: none;
+    height: 1px;
+    background-color: var(--separator-color);
+    margin: 2.5rem 0;
+    width: 100%;
+}
+
+/* ============================================
+   Section Headers - Clean without decorative underlines
+   ============================================ */
+h2 {
+    position: relative;
+    padding-bottom: 0;
+}
+
+/* Remove the short underline pseudo-element */
+h2::after {
+    display: none;
+}
+
+/* ============================================
+   Button Enhancements
+   ============================================ */
+.sd-btn-primary {
+    background: var(--hyperactive-gradient) !important;
+    border: none !important;
+    transition: all 0.3s ease !important;
+}
+
+.sd-btn-primary:hover {
+    background: var(--hyperactive-gradient-dark) !important;
+    transform: translateY(-1px);
+    box-shadow: 0 4px 12px rgba(93, 93, 122, 0.4);
+}
+
+/* ============================================
+   Responsive Adjustments
+   ============================================ */
+@media (max-width: 768px) {
+    .hero-section {
+        padding: 2rem 1rem;
+        margin: -1rem -0.5rem 1.5rem -0.5rem;
+    }
+
+    .hero-title {
+        font-size: 2.5rem;
+    }
+
+    .hero-tagline {
+        font-size: 1.4rem;
+    }
+
+    .hero-subtitle {
+        font-size: 1rem;
+    }
+
+    .maturity-items {
+        gap: 1rem;
+    }
+
+    .maturity-item {
+        font-size: 0.85rem;
+    }
+
+    .feature-icon {
+        font-size: 2rem;
+    }
+}
+
+@media (max-width: 480px) {
+    .hero-title {
+        font-size: 2rem;
+    }
+
+    .maturity-items {
+        flex-direction: column;
+        gap: 0.75rem;
+    }
+}
+
+/* ============================================
+   Print Styles
+   ============================================ */
+@media print {
+    .hero-section {
+        background: #f8f9fa !important;
+        color: #000 !important;
+    }
+
+    .hero-title,
+    .hero-tagline,
+    .hero-subtitle {
+        color: #000 !important;
+    }
+}
+
+/* ============================================
+   Vertical Tabs Component
+   ============================================ */
+.vertical-tabs {
+    display: flex;
+    gap: 1.5rem;
+    margin: 1.5rem 0;
+}
+
+.vertical-tabs-nav {
+    display: flex;
+    flex-direction: column;
+    width: 190px;
+    flex-shrink: 0;
+    background: var(--pst-color-background);
+    border-radius: 10px;
+    padding: 0.5rem;
+    box-shadow: var(--card-shadow);
+    border: 1px solid var(--pst-color-border);
+    align-self: flex-start;
+}
+
+.vertical-tab-btn {
+    display: flex;
+    align-items: center;
+    padding: 0.75rem 1rem;
+    background: transparent;
+    border: none;
+    border-left: 3px solid transparent;
+    border-radius: 6px;
+    text-align: left;
+    font-size: 0.875rem;
+    font-weight: 500;
+    color: var(--pst-color-text-muted);
+    cursor: pointer;
+    transition: all 0.2s ease;
+    position: relative;
+    margin: 2px 0;
+}
+
+.vertical-tab-btn:hover {
+    background: var(--pst-color-surface);
+    color: var(--pst-color-text-base);
+}
+
+.vertical-tab-btn.active {
+    border-left-color: var(--hyperactive-primary);
+    background: var(--pst-color-surface);
+    color: var(--hyperactive-primary);
+}
+
+html[data-theme="dark"] .vertical-tab-btn.active {
+    border-left-color: var(--hyperactive-secondary);
+    color: var(--hyperactive-secondary);
+}
+
+.vertical-tab-btn .tab-indicator {
+    width: 6px;
+    height: 6px;
+    border-radius: 50%;
+    background: var(--hyperactive-primary);
+    margin-right: 0.75rem;
+    opacity: 0;
+    transform: scale(0);
+    transition: all 0.2s ease;
+}
+
+.vertical-tab-btn.active .tab-indicator {
+    opacity: 1;
+    transform: scale(1);
+}
+
+html[data-theme="dark"] .vertical-tab-btn .tab-indicator {
+    background: var(--hyperactive-secondary);
+}
+
+.vertical-tabs-content {
+    flex: 1;
+    min-height: 200px;
+    overflow: hidden;
+}
+
+.vertical-tab-panel {
+    display: none;
+}
+
+.vertical-tab-panel.active {
+    display: block;
+    animation: revealVertical 0.35s ease-out;
+}
+
+@keyframes revealVertical {
+    from {
+        opacity: 0;
+        clip-path: inset(0 0 100% 0);
+    }
+    to {
+        opacity: 1;
+        clip-path: inset(0 0 0 0);
+    }
+}
+
+/* Code block styling within vertical tabs */
+.vertical-tab-panel .highlight {
+    margin: 0;
+    border-radius: 4px;
+    box-shadow: var(--card-shadow);
+    height: 380px;
+    overflow: hidden;
+}
+
+.vertical-tab-panel .highlight pre {
+    margin: 0;
+    padding: 1rem 1.25rem;
+    font-size: 0.85rem;
+    line-height: 1.5;
+    height: 100%;
+    overflow-y: auto;
+    border-radius: 4px;
+}
+
+/* Responsive: Convert to horizontal on smaller screens */
+@media (max-width: 768px) {
+    .vertical-tabs {
+        flex-direction: column;
+        gap: 1rem;
+    }
+
+    .vertical-tabs-nav {
+        flex-direction: row;
+        min-width: 100%;
+        overflow-x: auto;
+        padding: 0.25rem;
+        -webkit-overflow-scrolling: touch;
+        scrollbar-width: none;
+    }
+
+    .vertical-tabs-nav::-webkit-scrollbar {
+        display: none;
+    }
+
+    .vertical-tab-btn {
+        flex-shrink: 0;
+        border-left: none;
+        border-bottom: 3px solid transparent;
+        padding: 0.625rem 1rem;
+        white-space: nowrap;
+        margin: 0 2px;
+    }
+
+    .vertical-tab-btn.active {
+        border-left-color: transparent;
+        border-bottom-color: var(--hyperactive-primary);
+    }
+
+    html[data-theme="dark"] .vertical-tab-btn.active {
+        border-bottom-color: var(--hyperactive-secondary);
+    }
+
+    .vertical-tab-btn .tab-indicator {
+        display: none;
+    }
+}
+
+/* Section title styling for Quick Install/Example */
+.vertical-tabs-section-title {
+    font-size: 0.75rem;
+    font-weight: 600;
+    text-transform: uppercase;
+    letter-spacing: 0.05em;
+    color: var(--pst-color-text-muted);
+    padding: 0.75rem 1.25rem 0.5rem;
+    margin: 0;
+}
+
+/* Tab description subtitle */
+.vertical-tab-btn .tab-subtitle {
+    display: block;
+    font-size: 0.75rem;
+    font-weight: 400;
+    color: var(--pst-color-text-muted);
+    margin-top: 0.125rem;
+}
+
+.vertical-tab-btn.active .tab-subtitle {
+    color: var(--hyperactive-secondary);
+}
+
+html[data-theme="dark"] .vertical-tab-btn.active .tab-subtitle {
+    color: var(--hyperactive-accent);
+}
+
+/* ============================================
+   Segmented Control Tabs (for Quick Install)
+   ============================================ */
+.segmented-tabs {
+    margin: 1.5rem 0;
+}
+
+.segmented-tabs-nav {
+    display: inline-flex;
+    background: var(--pst-color-background);
+    border-radius: 10px;
+    padding: 4px;
+    gap: 2px;
+    box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.08);
+    border: 1px solid var(--pst-color-border);
+}
+
+.segmented-tab-btn {
+    padding: 0.5rem 1.25rem;
+    border: none;
+    background: transparent;
+    border-radius: 8px;
+    font-size: 0.875rem;
+    font-weight: 500;
+    color: var(--pst-color-text-muted);
+    cursor: pointer;
+    transition: all 0.2s ease;
+    white-space: nowrap;
+}
+
+.segmented-tab-btn:hover {
+    color: var(--pst-color-text-base);
+}
+
+.segmented-tab-btn.active {
+    background: var(--pst-color-surface);
+    color: var(--hyperactive-primary);
+    box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
+}
+
+html[data-theme="dark"] .segmented-tab-btn.active {
+    color: var(--hyperactive-secondary);
+    box-shadow: 0 1px 3px rgba(0, 0, 0, 0.3);
+}
+
+.segmented-tabs-content {
+    margin-top: 1rem;
+}
+
+.segmented-tab-panel {
+    display: none;
+    animation: segmentedFadeIn 0.2s ease;
+}
+
+.segmented-tab-panel.active {
+    display: block;
+}
+
+@keyframes segmentedFadeIn {
+    from {
+        opacity: 0;
+        transform: translateY(-4px);
+    }
+    to {
+        opacity: 1;
+        transform: translateY(0);
+    }
+}
+
+.segmented-tab-panel .highlight {
+    border-radius: 8px;
+    margin: 0;
+}
+
+.segmented-tab-panel .highlight pre {
+    margin: 0;
+    padding: 0.875rem 1.25rem;
+    font-size: 0.9rem;
+}
+
+/* Responsive: stack on very small screens */
+@media (max-width: 540px) {
+    .segmented-tabs-nav {
+        flex-wrap: wrap;
+        width: 100%;
+    }
+
+    .segmented-tab-btn {
+        flex: 1 1 auto;
+        text-align: center;
+        padding: 0.5rem 0.75rem;
+    }
+}
diff --git a/docs/source/_static/fields.css b/docs/source/_static/fields.css
new file mode 100644
index 00000000..48ec4964
--- /dev/null
+++ b/docs/source/_static/fields.css
@@ -0,0 +1,21 @@
+/* CSS for parameter fields and other structured documentation elements */
+
+/* Styling for parameter lists */
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 0.5em;
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-bottom: 0.5em;
+    margin-left: 0em;
+}
diff --git a/docs/source/_static/images/badges/generate_badges.py b/docs/source/_static/images/badges/generate_badges.py
new file mode 100644
index 00000000..96fb09c3
--- /dev/null
+++ b/docs/source/_static/images/badges/generate_badges.py
@@ -0,0 +1,188 @@
+#!/usr/bin/env python3
+"""
+Badge Generator for Hyperactive Documentation
+
+Generates local SVG badges based on project information from pyproject.toml.
+This eliminates dependency on external badge services like shields.io.
+
+Usage:
+    python generate_badges.py
+
+Output:
+    - version.svg
+    - python.svg
+    - license.svg
+    - sponsor.svg
+"""
+
+import re
+from pathlib import Path
+
+try:
+    import tomllib
+except ImportError:
+    import tomli as tomllib
+
+
+# Hyperactive color palette
+COLORS = {
+    "primary": "#5D5D7A",
+    "secondary": "#7070A0",
+    "dark": "#4A4A65",
+    "light": "#f8f9fa",
+    "sponsor": "#0eac92",
+    "label_bg": "#555",
+}
+
+
+def create_badge_svg(
+    label: str, value: str, color: str, label_width: int = None, value_width: int = None
+) -> str:
+    """
+    Create an SVG badge in shields.io flat-square style.
+
+    Args:
+        label: Left side text (e.g., "version")
+        value: Right side text (e.g., "5.0.2")
+        color: Hex color for the value background
+        label_width: Override calculated label width
+        value_width: Override calculated value width
+
+    Returns
+    -------
+        SVG string
+    """
+    # Approximate width calculation (7px per character + padding)
+    char_width = 6.5
+    padding = 10
+
+    lw = label_width or int(len(label) * char_width + padding * 2)
+    vw = value_width or int(len(value) * char_width + padding * 2)
+    total_width = lw + vw
+    height = 20
+
+    svg = f"""<svg xmlns="http://www.w3.org/2000/svg" width="{total_width}" height="{height}">
+  <title>{label}: {value}</title>
+  <!-- Label background -->
+  <rect width="{lw}" height="{height}" fill="{COLORS['label_bg']}"/>
+  <!-- Value background -->
+  <rect x="{lw}" width="{vw}" height="{height}" fill="{color}"/>
+  <!-- Label text -->
+  <text x="{lw/2}" y="14" fill="#fff" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11" text-anchor="middle">{label}</text>
+  <!-- Value text -->
+  <text x="{lw + vw/2}" y="14" fill="#fff" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11" text-anchor="middle">{value}</text>
+</svg>"""
+
+    return svg
+
+
+def create_simple_badge_svg(
+    text: str, color: str, width: int = None, font_size: int = 11
+) -> str:
+    """
+    Create a simple single-section SVG badge.
+
+    Args:
+        text: Badge text
+        color: Background color
+        width: Override calculated width
+        font_size: Font size in pixels
+
+    Returns
+    -------
+        SVG string
+    """
+    char_width = 7
+    padding = 16
+    w = width or int(len(text) * char_width + padding * 2)
+    height = 20
+    text_y = 14 if font_size >= 11 else 13.5
+
+    svg = f"""<svg xmlns="http://www.w3.org/2000/svg" width="{w}" height="{height}">
+  <title>{text}</title>
+  <rect width="{w}" height="{height}" fill="{color}"/>
+  <text x="{w/2}" y="{text_y}" fill="#fff" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="{font_size}" text-anchor="middle" font-weight="600">{text}</text>
+</svg>"""
+
+    return svg
+
+
+def extract_python_versions(classifiers: list) -> str:
+    """Extract Python version range from classifiers."""
+    versions = []
+    pattern = r"Programming Language :: Python :: (\d+\.\d+)"
+
+    for classifier in classifiers:
+        match = re.match(pattern, classifier)
+        if match:
+            versions.append(match.group(1))
+
+    if not versions:
+        return "3.10+"
+
+    versions.sort(key=lambda v: tuple(map(int, v.split("."))))
+
+    if len(versions) == 1:
+        return versions[0]
+
+    return f"{versions[0]} - {versions[-1]}"
+
+
+def main():
+    # Find pyproject.toml
+    script_dir = Path(__file__).parent
+    project_root = script_dir.parents[4]  # Go up from docs/source/_static/images/badges
+    pyproject_path = project_root / "pyproject.toml"
+
+    if not pyproject_path.exists():
+        print(f"Error: pyproject.toml not found at {pyproject_path}")
+        return
+
+    # Read project info
+    with open(pyproject_path, "rb") as f:
+        pyproject = tomllib.load(f)
+
+    project = pyproject.get("project", {})
+    version = project.get("version", "0.0.0")
+    classifiers = project.get("classifiers", [])
+
+    # Extract license from classifiers
+    license_name = "MIT"  # Default
+    for classifier in classifiers:
+        if "License :: OSI Approved ::" in classifier:
+            license_name = classifier.split("::")[-1].strip()
+            # Shorten common license names
+            if "MIT" in license_name:
+                license_name = "MIT"
+            elif "BSD" in license_name:
+                license_name = "BSD"
+            elif "Apache" in license_name:
+                license_name = "Apache 2.0"
+            break
+
+    python_range = extract_python_versions(classifiers)
+
+    print(f"Generating badges for Hyperactive v{version}")
+    print(f"  Python: {python_range}")
+    print(f"  License: {license_name}")
+
+    # Generate badges
+    badges = {
+        "version.svg": create_badge_svg("version", f"v{version}", COLORS["primary"]),
+        "python.svg": create_badge_svg("python", python_range, COLORS["primary"]),
+        "license.svg": create_badge_svg("license", license_name, COLORS["primary"]),
+        "sponsor.svg": create_badge_svg("GC.OS", "Sponsored", COLORS["sponsor"]),
+    }
+
+    # Write badges
+    for filename, svg_content in badges.items():
+        filepath = script_dir / filename
+        with open(filepath, "w") as f:
+            f.write(svg_content)
+        print(f"  Created: {filename}")
+
+    print("\nDone! Badges generated successfully.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/source/_static/images/badges/license.svg b/docs/source/_static/images/badges/license.svg
new file mode 100644
index 00000000..ed760277
--- /dev/null
+++ b/docs/source/_static/images/badges/license.svg
@@ -0,0 +1,11 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="104" height="20">
+  <title>license: MIT</title>
+  <!-- Label background -->
+  <rect width="65" height="20" fill="#555"/>
+  <!-- Value background -->
+  <rect x="65" width="39" height="20" fill="#5D5D7A"/>
+  <!-- Label text -->
+  <text x="32.5" y="14" fill="#fff" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11" text-anchor="middle">license</text>
+  <!-- Value text -->
+  <text x="84.5" y="14" fill="#fff" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11" text-anchor="middle">MIT</text>
+</svg>
diff --git a/docs/source/_static/images/badges/python.svg b/docs/source/_static/images/badges/python.svg
new file mode 100644
index 00000000..855acfcf
--- /dev/null
+++ b/docs/source/_static/images/badges/python.svg
@@ -0,0 +1,11 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="150" height="20">
+  <title>python: 3.10 - 3.14</title>
+  <!-- Label background -->
+  <rect width="59" height="20" fill="#555"/>
+  <!-- Value background -->
+  <rect x="59" width="91" height="20" fill="#5D5D7A"/>
+  <!-- Label text -->
+  <text x="29.5" y="14" fill="#fff" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11" text-anchor="middle">python</text>
+  <!-- Value text -->
+  <text x="104.5" y="14" fill="#fff" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11" text-anchor="middle">3.10 - 3.14</text>
+</svg>
diff --git a/docs/source/_static/images/badges/sponsor.svg b/docs/source/_static/images/badges/sponsor.svg
new file mode 100644
index 00000000..b140d8ec
--- /dev/null
+++ b/docs/source/_static/images/badges/sponsor.svg
@@ -0,0 +1,11 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="130" height="20">
+  <title>GC.OS: Sponsored</title>
+  <!-- Label background -->
+  <rect width="52" height="20" fill="#555"/>
+  <!-- Value background -->
+  <rect x="52" width="78" height="20" fill="#0eac92"/>
+  <!-- Label text -->
+  <text x="26.0" y="14" fill="#fff" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11" text-anchor="middle">GC.OS</text>
+  <!-- Value text -->
+  <text x="91.0" y="14" fill="#fff" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11" text-anchor="middle">Sponsored</text>
+</svg>
diff --git a/docs/source/_static/images/badges/version.svg b/docs/source/_static/images/badges/version.svg
new file mode 100644
index 00000000..8ed5094b
--- /dev/null
+++ b/docs/source/_static/images/badges/version.svg
@@ -0,0 +1,11 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="124" height="20">
+  <title>version: v5.0.2</title>
+  <!-- Label background -->
+  <rect width="65" height="20" fill="#555"/>
+  <!-- Value background -->
+  <rect x="65" width="59" height="20" fill="#5D5D7A"/>
+  <!-- Label text -->
+  <text x="32.5" y="14" fill="#fff" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11" text-anchor="middle">version</text>
+  <!-- Value text -->
+  <text x="94.5" y="14" fill="#fff" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11" text-anchor="middle">v5.0.2</text>
+</svg>
diff --git a/docs/source/_static/images/bayes_convex.gif b/docs/source/_static/images/bayes_convex.gif
new file mode 100644
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diff --git a/docs/source/_templates/class.rst b/docs/source/_templates/class.rst
new file mode 100644
index 00000000..17d61dc4
--- /dev/null
+++ b/docs/source/_templates/class.rst
@@ -0,0 +1,10 @@
+{{objname}}
+{{ underline }}==============
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+
+.. raw:: html
+
+    <div class="clearer"></div>
diff --git a/docs/source/_templates/function.rst b/docs/source/_templates/function.rst
new file mode 100644
index 00000000..54226b73
--- /dev/null
+++ b/docs/source/_templates/function.rst
@@ -0,0 +1,10 @@
+{{objname}}
+{{ underline }}====================
+
+.. currentmodule:: {{ module }}
+
+.. autofunction:: {{ objname }}
+
+.. raw:: html
+
+    <div class="clearer"></div>
diff --git a/docs/source/_templates/layout.html b/docs/source/_templates/layout.html
new file mode 100644
index 00000000..b43b96a9
--- /dev/null
+++ b/docs/source/_templates/layout.html
@@ -0,0 +1,6 @@
+{% extends "pydata_sphinx_theme/layout.html" %}
+
+{% block extrahead %}
+{{ super() }}
+<meta name="python-version-range" content="{{ python_version_range }}">
+{% endblock %}
diff --git a/docs/source/about.rst b/docs/source/about.rst
new file mode 100644
index 00000000..7b342c8b
--- /dev/null
+++ b/docs/source/about.rst
@@ -0,0 +1,107 @@
+.. _about:
+
+=====
+About
+=====
+
+Hyperactive is an optimization and data collection toolbox for convenient and fast
+prototyping of computationally expensive models.
+
+.. toctree::
+   :maxdepth: 1
+
+   about/team
+   about/history
+   about/license
+
+
+About Hyperactive
+-----------------
+
+Hyperactive provides a unified interface for hyperparameter optimization using
+various gradient-free optimization algorithms. It supports optimization for
+scikit-learn, sktime, skpro, and PyTorch Lightning models, as well as custom
+objective functions.
+
+
+Mission
+^^^^^^^
+
+Hyperactive aims to make hyperparameter optimization accessible and practical for
+machine learning practitioners. By providing a unified API across many optimization
+algorithms and ML frameworks, it reduces the barrier to finding optimal model
+configurations.
+
+
+Key Features
+^^^^^^^^^^^^
+
+- **20+ Optimization Algorithms**: From simple hill climbing to advanced Bayesian
+  optimization, population methods, and Optuna integration.
+
+- **Experiment-Based Architecture**: Clean separation between what to optimize
+  (experiments) and how to optimize (algorithms).
+
+- **Framework Integrations**: First-class support for scikit-learn, sktime, skpro,
+  and PyTorch Lightning.
+
+- **Flexible Search Spaces**: Discrete, continuous, and mixed parameter spaces
+  using familiar NumPy/list syntax.
+
+- **Production Ready**: Battle-tested since 2019 with comprehensive testing and
+  active maintenance.
+
+
+Related Projects
+^^^^^^^^^^^^^^^^
+
+Hyperactive is part of a larger ecosystem:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Project
+     - Description
+   * - `Gradient-Free-Optimizers <https://github.com/SimonBlanke/Gradient-Free-Optimizers>`_
+     - Core optimization algorithms used by Hyperactive
+   * - `Search-Data-Collector <https://github.com/SimonBlanke/search-data-collector>`_
+     - Save search data during optimization to CSV files
+   * - `Search-Data-Explorer <https://github.com/SimonBlanke/search-data-explorer>`_
+     - Visualize search data with Plotly in a Streamlit dashboard
+
+
+Sponsorship
+^^^^^^^^^^^
+
+Hyperactive is sponsored by the
+`German Center for Open Source AI (GC.OS) <https://gc-os-ai.github.io/>`_.
+
+.. image:: https://img.shields.io/badge/GC.OS-Sponsored%20Project-orange.svg?style=for-the-badge&colorA=0eac92&colorB=2077b4
+   :target: https://gc-os-ai.github.io/
+   :alt: GC.OS Sponsored
+
+
+Citing Hyperactive
+^^^^^^^^^^^^^^^^^^
+
+If you use Hyperactive in your research, please cite it:
+
+.. code-block:: bibtex
+
+    @Misc{hyperactive2021,
+      author =   {{Simon Blanke}},
+      title =    {{Hyperactive}: An optimization and data collection toolbox
+                  for convenient and fast prototyping of computationally
+                  expensive models.},
+      howpublished = {\url{https://github.com/SimonBlanke}},
+      year = {since 2019}
+    }
+
+
+Community
+^^^^^^^^^
+
+- **GitHub**: `SimonBlanke/Hyperactive <https://github.com/SimonBlanke/Hyperactive>`_
+- **Discord**: `Join the community <https://discord.gg/7uKdHfdcJG>`_
+- **LinkedIn**: `German Center for Open Source AI <https://www.linkedin.com/company/german-center-for-open-source-ai>`_
diff --git a/docs/source/about/history.rst b/docs/source/about/history.rst
new file mode 100644
index 00000000..ce3033ff
--- /dev/null
+++ b/docs/source/about/history.rst
@@ -0,0 +1,126 @@
+.. _history:
+
+=======
+History
+=======
+
+This page documents the history and evolution of Hyperactive.
+
+
+Project History
+---------------
+
+Hyperactive was created in 2018 by Simon Blanke to address the need for a flexible,
+unified interface for hyperparameter optimization in machine learning workflows.
+
+
+Timeline
+^^^^^^^^
+
+**2018 - Project Creation**
+    Hyperactive was first released as an open-source project, providing a collection
+    of gradient-free optimization algorithms accessible through a simple Python API.
+
+**2019 - Growing Adoption**
+    The project gained traction in the machine learning community, with users
+    appreciating its straightforward interface and variety of optimization algorithms.
+
+**2020-2021 - Ecosystem Expansion**
+    Related projects were developed to complement Hyperactive:
+
+    - **Gradient-Free-Optimizers**: The optimization backend was extracted into its
+      own package, allowing for more modular development.
+    - **Search-Data-Collector**: Tools for saving optimization results.
+    - **Search-Data-Explorer**: Visualization dashboard for exploring search data.
+
+**2022-2023 - Continued Development**
+    Active maintenance continued with bug fixes, new algorithms, and improved
+    documentation. The user base continued to grow.
+
+**2024 - Version 5.0 Redesign**
+    Major architecture redesign introducing:
+
+    - **Experiment-based architecture**: Clean separation between optimization
+      problems (experiments) and optimization algorithms (optimizers).
+    - **Enhanced integrations**: Improved support for scikit-learn, sktime, skpro,
+      and PyTorch Lightning.
+    - **Optuna backend**: Integration with Optuna's optimization algorithms.
+    - **Modern Python support**: Support for Python 3.10 through 3.14.
+
+**2024 - GC.OS Sponsorship**
+    Hyperactive became a sponsored project of the German Center for Open Source AI
+    (GC.OS), ensuring continued development and maintenance.
+
+
+Version History
+---------------
+
+Major Versions
+^^^^^^^^^^^^^^
+
+.. list-table::
+   :header-rows: 1
+   :widths: 15 85
+
+   * - Version
+     - Highlights
+   * - v5.0
+     - Experiment-based architecture, Optuna integration, modern Python support
+   * - v4.x
+     - Improved API stability, additional optimizers
+   * - v3.x
+     - Search data collection features, expanded algorithm library
+   * - v2.x
+     - Multi-processing support, warm starting
+   * - v1.x
+     - Initial public release with core optimization algorithms
+
+
+Breaking Changes
+^^^^^^^^^^^^^^^^
+
+Major version updates (e.g., v4 → v5) may include breaking API changes.
+If you're upgrading from an older version:
+
+1. Check the `GitHub releases <https://github.com/SimonBlanke/Hyperactive/releases>`_
+   for migration guides.
+2. Update your code to use the new API patterns.
+3. Alternatively, pin your version to continue using the old API.
+
+.. code-block:: bash
+
+    # Upgrade to latest
+    pip install hyperactive --upgrade
+
+    # Or pin to specific version
+    pip install hyperactive==4.x.x
+
+
+Legacy Documentation
+^^^^^^^^^^^^^^^^^^^^
+
+Documentation for Hyperactive v4 is still available at the legacy documentation site:
+
+`Legacy Documentation (v4) <https://simonblanke.github.io/hyperactive-documentation/5.0/>`_
+
+This may be useful if you:
+
+- Are maintaining projects that use Hyperactive v4
+- Need to reference the previous API design
+- Want to compare the old and new approaches
+
+
+Future Roadmap
+--------------
+
+Hyperactive continues to evolve. Planned improvements include:
+
+- Additional optimization algorithms
+- Enhanced visualization tools
+- Improved distributed computing support
+- More framework integrations
+- Performance optimizations
+
+For the latest roadmap, see the
+`GitHub Issues <https://github.com/SimonBlanke/Hyperactive/issues>`_ and
+`Discussions <https://github.com/SimonBlanke/Hyperactive/discussions>`_.
diff --git a/docs/source/about/license.rst b/docs/source/about/license.rst
new file mode 100644
index 00000000..498ed9ad
--- /dev/null
+++ b/docs/source/about/license.rst
@@ -0,0 +1,99 @@
+.. _license:
+
+=======
+License
+=======
+
+Hyperactive is open-source software released under the MIT License.
+
+
+MIT License
+-----------
+
+.. code-block:: text
+
+    MIT License
+
+    Copyright (c) 2018 Simon Blanke
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all
+    copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+    SOFTWARE.
+
+
+What This Means
+---------------
+
+The MIT License is a permissive open-source license that allows you to:
+
+**You Can:**
+
+- Use Hyperactive for commercial projects
+- Modify the source code
+- Distribute copies
+- Use it in private projects
+- Sublicense the code
+
+**Requirements:**
+
+- Include the copyright notice and license text in any copies or substantial
+  portions of the software
+
+**Limitations:**
+
+- The software is provided "as is" without warranty
+- The authors are not liable for any damages
+
+
+Third-Party Licenses
+--------------------
+
+Hyperactive depends on several third-party packages, each with their own licenses:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 30 40
+
+   * - Package
+     - License
+     - Purpose
+   * - NumPy
+     - BSD-3-Clause
+     - Numerical operations
+   * - pandas
+     - BSD-3-Clause
+     - Data manipulation
+   * - scikit-learn
+     - BSD-3-Clause
+     - Machine learning integration
+   * - Gradient-Free-Optimizers
+     - MIT
+     - Core optimization algorithms
+   * - tqdm
+     - MIT/MPL-2.0
+     - Progress bars
+
+All dependencies use permissive open-source licenses compatible with commercial use.
+
+
+Questions
+---------
+
+For questions about licensing or usage rights, please contact:
+
+- **Email**: simon.blanke@yahoo.com
+- **GitHub Issues**: `Report an issue <https://github.com/SimonBlanke/Hyperactive/issues>`_
diff --git a/docs/source/about/team.rst b/docs/source/about/team.rst
new file mode 100644
index 00000000..352c4006
--- /dev/null
+++ b/docs/source/about/team.rst
@@ -0,0 +1,102 @@
+.. _team:
+
+====
+Team
+====
+
+Hyperactive is developed and maintained by a dedicated team of open-source contributors.
+
+
+Core Team
+---------
+
+Simon Blanke
+^^^^^^^^^^^^
+
+**Creator and Lead Maintainer**
+
+Simon Blanke is the creator and primary maintainer of Hyperactive. He started the
+project in 2018 to make hyperparameter optimization more accessible to machine
+learning practitioners.
+
+- **GitHub**: `@SimonBlanke <https://github.com/SimonBlanke>`_
+- **Email**: simon.blanke@yahoo.com
+
+Simon also maintains the related projects in the Hyperactive ecosystem:
+
+- `Gradient-Free-Optimizers <https://github.com/SimonBlanke/Gradient-Free-Optimizers>`_
+- `Search-Data-Collector <https://github.com/SimonBlanke/search-data-collector>`_
+- `Search-Data-Explorer <https://github.com/SimonBlanke/search-data-explorer>`_
+
+
+Contributors
+------------
+
+Hyperactive benefits from contributions by many developers. We appreciate everyone
+who has helped improve the project through code, documentation, bug reports, and
+feature suggestions.
+
+To see a full list of contributors, visit the
+`GitHub contributors page <https://github.com/SimonBlanke/Hyperactive/graphs/contributors>`_.
+
+
+Become a Contributor
+^^^^^^^^^^^^^^^^^^^^
+
+We welcome contributions from the community! There are many ways to get involved:
+
+- **Report bugs**: Open an issue on GitHub
+- **Suggest features**: Share your ideas in GitHub Discussions
+- **Contribute code**: Submit a pull request
+- **Improve documentation**: Help make the docs better
+- **Share examples**: Contribute use cases and tutorials
+
+See the :ref:`contributing` guide for more information on how to contribute.
+
+
+Sponsorship
+-----------
+
+German Center for Open Source AI (GC.OS)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Hyperactive is proudly sponsored by the
+`German Center for Open Source AI (GC.OS) <https://gc-os-ai.github.io/>`_.
+
+.. image:: https://img.shields.io/badge/GC.OS-Sponsored%20Project-orange.svg?style=for-the-badge&colorA=0eac92&colorB=2077b4
+   :target: https://gc-os-ai.github.io/
+   :alt: GC.OS Sponsored
+
+This sponsorship helps ensure the continued development and maintenance of
+Hyperactive as a high-quality open-source project.
+
+
+Community
+---------
+
+Join the Hyperactive community:
+
+- **Discord**: `Join our Discord server <https://discord.gg/7uKdHfdcJG>`_
+  for discussions, questions, and announcements.
+
+- **LinkedIn**: Follow the
+  `German Center for Open Source AI <https://www.linkedin.com/company/german-center-for-open-source-ai>`_
+  for updates.
+
+- **GitHub Discussions**: Participate in
+  `discussions <https://github.com/SimonBlanke/Hyperactive/discussions>`_
+  about features and best practices.
+
+
+Acknowledgments
+---------------
+
+We thank all users who have:
+
+- Reported bugs and issues
+- Suggested new features
+- Contributed to the codebase
+- Helped improve the documentation
+- Shared Hyperactive with others
+
+Your support makes this project possible!
diff --git a/docs/source/api_reference.rst b/docs/source/api_reference.rst
new file mode 100644
index 00000000..7f5996f7
--- /dev/null
+++ b/docs/source/api_reference.rst
@@ -0,0 +1,21 @@
+.. _api_reference:
+
+=============
+API Reference
+=============
+
+Welcome to the API reference for ``hyperactive``.
+
+The API reference provides a technical manual.
+It describes the classes and functions included in Hyperactive.
+
+.. toctree::
+    :maxdepth: 1
+
+    api_reference/base
+    api_reference/optimizers
+    api_reference/experiments_function
+    api_reference/experiments_integrations
+    api_reference/experiments_benchmarks
+    api_reference/sklearn_integration
+    api_reference/utilities
diff --git a/docs/source/api_reference/base.rst b/docs/source/api_reference/base.rst
new file mode 100644
index 00000000..a7dca433
--- /dev/null
+++ b/docs/source/api_reference/base.rst
@@ -0,0 +1,17 @@
+.. _base_ref:
+
+Base Classes
+============
+
+The :mod:`hyperactive.base` module contains the base classes for optimizers and experiments.
+
+All optimizers inherit from ``BaseOptimizer`` and all experiments inherit from ``BaseExperiment``.
+
+.. currentmodule:: hyperactive.base
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    BaseOptimizer
+    BaseExperiment
diff --git a/docs/source/api_reference/experiments_benchmarks.rst b/docs/source/api_reference/experiments_benchmarks.rst
new file mode 100644
index 00000000..0c46dba5
--- /dev/null
+++ b/docs/source/api_reference/experiments_benchmarks.rst
@@ -0,0 +1,19 @@
+.. _experiments_benchmarks_ref:
+
+Benchmark Function Experiments
+===============================
+
+The :mod:`hyperactive.experiment.bench` module contains standard benchmark functions
+for testing and comparing optimization algorithms.
+
+These mathematical functions have known properties and are commonly used in optimization research.
+
+.. currentmodule:: hyperactive.experiment.bench
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    Ackley
+    Parabola
+    Sphere
diff --git a/docs/source/api_reference/experiments_function.rst b/docs/source/api_reference/experiments_function.rst
new file mode 100644
index 00000000..bef832ae
--- /dev/null
+++ b/docs/source/api_reference/experiments_function.rst
@@ -0,0 +1,20 @@
+.. _experiments_function_ref:
+
+Function Experiments
+====================
+
+The :mod:`hyperactive.experiment` module contains experiment classes for defining
+optimization objectives.
+
+Generic Function Experiment
+----------------------------
+
+The ``FunctionExperiment`` class allows you to wrap any callable as an optimization target.
+
+.. currentmodule:: hyperactive.experiment.func
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    FunctionExperiment
diff --git a/docs/source/api_reference/experiments_integrations.rst b/docs/source/api_reference/experiments_integrations.rst
new file mode 100644
index 00000000..913e249a
--- /dev/null
+++ b/docs/source/api_reference/experiments_integrations.rst
@@ -0,0 +1,57 @@
+.. _experiments_integrations_ref:
+
+Framework Integration Experiments
+==================================
+
+The :mod:`hyperactive.experiment.integrations` module contains experiment classes
+for integration with machine learning frameworks.
+
+These experiments provide seamless hyperparameter optimization for scikit-learn,
+sktime, skpro, and PyTorch Lightning models.
+
+Scikit-Learn
+------------
+
+Cross-validation experiments for scikit-learn estimators.
+
+.. currentmodule:: hyperactive.experiment.integrations
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    SklearnCvExperiment
+
+Sktime - Time Series
+--------------------
+
+Experiments for sktime time series models.
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    SktimeClassificationExperiment
+    SktimeForecastingExperiment
+
+Skpro - Probabilistic Prediction
+---------------------------------
+
+Experiments for skpro probabilistic regression models.
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    SkproProbaRegExperiment
+
+PyTorch Lightning
+-----------------
+
+Experiments for PyTorch Lightning models.
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    TorchExperiment
diff --git a/docs/source/api_reference/optimizers.rst b/docs/source/api_reference/optimizers.rst
new file mode 100644
index 00000000..ab9f0224
--- /dev/null
+++ b/docs/source/api_reference/optimizers.rst
@@ -0,0 +1,128 @@
+.. _optimizers_ref:
+
+Optimizers
+==========
+
+The :mod:`hyperactive.opt` module contains optimization algorithms for hyperparameter tuning.
+
+All optimizers inherit from :class:`hyperactive.base.BaseOptimizer` and share the same interface:
+the ``solve()`` method to run optimization, and configuration via the ``experiment`` and ``search_space`` parameters.
+
+Gradient-Free Optimizers (GFO)
+-------------------------------
+
+These optimizers are based on the gradient-free-optimizers package and implement
+various metaheuristic and numerical optimization algorithms.
+
+Local Search
+~~~~~~~~~~~~
+
+Local search algorithms that explore the neighborhood of the current position.
+
+.. currentmodule:: hyperactive.opt
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    HillClimbing
+    StochasticHillClimbing
+    RepulsingHillClimbing
+    RandomRestartHillClimbing
+
+Simulated Annealing
+~~~~~~~~~~~~~~~~~~~
+
+Probabilistic techniques for approximating the global optimum.
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    SimulatedAnnealing
+
+Global Search
+~~~~~~~~~~~~~
+
+Random and systematic search strategies.
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    RandomSearch
+    GridSearch
+
+Direct Methods
+~~~~~~~~~~~~~~
+
+Direct search methods that do not use gradients.
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    DownhillSimplexOptimizer
+    PowellsMethod
+    PatternSearch
+    LipschitzOptimizer
+    DirectAlgorithm
+
+Population-Based
+~~~~~~~~~~~~~~~~
+
+Optimization algorithms that maintain and evolve populations of solutions.
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    ParallelTempering
+    ParticleSwarmOptimizer
+    SpiralOptimization
+    GeneticAlgorithm
+    EvolutionStrategy
+    DifferentialEvolution
+
+Sequential Model-Based
+~~~~~~~~~~~~~~~~~~~~~~
+
+Algorithms that build surrogate models of the objective function.
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    BayesianOptimizer
+    TreeStructuredParzenEstimators
+    ForestOptimizer
+
+Optuna-Based Optimizers
+------------------------
+
+These optimizers provide an interface to Optuna's optimization algorithms.
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    TPEOptimizer
+    RandomOptimizer
+    CmaEsOptimizer
+    GPOptimizer
+    GridOptimizer
+    NSGAIIOptimizer
+    NSGAIIIOptimizer
+    QMCOptimizer
+
+Scikit-Learn Style
+-------------------
+
+Optimizers with a scikit-learn compatible interface.
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    GridSearchSk
+    RandomSearchSk
diff --git a/docs/source/api_reference/sklearn_integration.rst b/docs/source/api_reference/sklearn_integration.rst
new file mode 100644
index 00000000..82e454de
--- /dev/null
+++ b/docs/source/api_reference/sklearn_integration.rst
@@ -0,0 +1,18 @@
+.. _sklearn_integration_ref:
+
+Scikit-Learn Integration
+=========================
+
+The :mod:`hyperactive.integrations.sklearn` module provides scikit-learn compatible
+meta-estimators for hyperparameter optimization.
+
+These classes follow the scikit-learn estimator API and can be used as drop-in replacements
+for scikit-learn's GridSearchCV and RandomizedSearchCV.
+
+.. currentmodule:: hyperactive.integrations.sklearn
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    OptCV
diff --git a/docs/source/api_reference/utilities.rst b/docs/source/api_reference/utilities.rst
new file mode 100644
index 00000000..c531e044
--- /dev/null
+++ b/docs/source/api_reference/utilities.rst
@@ -0,0 +1,18 @@
+.. _utilities_ref:
+
+Utilities
+=========
+
+The :mod:`hyperactive.utils` module contains utility functions for working with
+Hyperactive estimators.
+
+Estimator Validation
+--------------------
+
+.. currentmodule:: hyperactive.utils
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: function.rst
+
+    check_estimator
diff --git a/docs/source/conf.py b/docs/source/conf.py
new file mode 100644
index 00000000..de3e263e
--- /dev/null
+++ b/docs/source/conf.py
@@ -0,0 +1,435 @@
+#!/usr/bin/env python3
+"""Configuration file for the Sphinx documentation builder."""
+
+import datetime
+import os
+import re
+import sys
+from pathlib import Path
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+
+ON_READTHEDOCS = os.environ.get("READTHEDOCS") == "True"
+if not ON_READTHEDOCS:
+    sys.path.insert(0, os.path.abspath("../../src"))
+
+import hyperactive  # noqa: E402  # must be after sys.path modification
+
+# -- Extract metadata from pyproject.toml ------------------------------------
+# This allows documentation to stay in sync with pyproject.toml automatically
+
+
+def extract_pyproject_metadata():
+    """Extract metadata from pyproject.toml for use in documentation."""
+    pyproject_path = Path(__file__).parent.parent.parent / "pyproject.toml"
+
+    metadata = {
+        "python_versions": [],
+        "min_python": "3.10",
+        "dependencies": [],
+        "version": hyperactive.__version__,
+    }
+
+    if pyproject_path.exists():
+        content = pyproject_path.read_text()
+
+        # Extract Python versions from classifiers
+        # Pattern: "Programming Language :: Python :: 3.XX"
+        py_version_pattern = r'"Programming Language :: Python :: (3\.\d+)"'
+        versions = re.findall(py_version_pattern, content)
+        if versions:
+            metadata["python_versions"] = sorted(set(versions))
+
+        # Extract requires-python
+        requires_python_match = re.search(r'requires-python\s*=\s*"([^"]+)"', content)
+        if requires_python_match:
+            req = requires_python_match.group(1)
+            # Extract minimum version from ">=3.10" or similar
+            min_match = re.search(r">=\s*([\d.]+)", req)
+            if min_match:
+                metadata["min_python"] = min_match.group(1)
+
+        # Extract core dependencies
+        deps_match = re.search(r"dependencies\s*=\s*\[(.*?)\]", content, re.DOTALL)
+        if deps_match:
+            deps_text = deps_match.group(1)
+            # Extract package names (first word before any version specifier)
+            dep_names = re.findall(r'"([a-zA-Z][a-zA-Z0-9_-]*)', deps_text)
+            metadata["dependencies"] = dep_names
+
+    return metadata
+
+
+# Extract metadata once at configuration time
+PYPROJECT_METADATA = extract_pyproject_metadata()
+
+# Build Python version range string from metadata
+_py_versions = PYPROJECT_METADATA["python_versions"]
+if _py_versions:
+    _py_version_range = f"{_py_versions[0]} - {_py_versions[-1]}"
+else:
+    _py_version_range = "3.10+"
+
+# -- Project information -----------------------------------------------------
+current_year = datetime.datetime.now().year
+project = "hyperactive"
+project_copyright = f"2019 - {current_year} (MIT License)"
+author = "Simon Blanke"
+
+# The full version, including alpha/beta/rc tags
+CURRENT_VERSION = f"v{hyperactive.__version__}"
+
+# If on readthedocs, and we're building the latest version, update tag to generate
+# correct links in notebooks
+if ON_READTHEDOCS:
+    READTHEDOCS_VERSION = os.environ.get("READTHEDOCS_VERSION")
+    if READTHEDOCS_VERSION == "latest":
+        CURRENT_VERSION = "main"
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.autosectionlabel",
+    "numpydoc",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.linkcode",  # link to GitHub source code via linkcode_resolve()
+    "myst_parser",
+    "sphinx_copybutton",
+    "sphinx_design",
+    "sphinx_issues",
+    "sphinx.ext.doctest",
+]
+
+# Recommended by sphinx_design when using the MyST Parser
+myst_enable_extensions = ["colon_fence"]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+source_suffix = {
+    ".rst": "restructuredtext",
+    ".md": "markdown",
+}
+
+# The main toctree document.
+master_doc = "index"
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = "en"
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = [
+    "_build",
+    ".ipynb_checkpoints",
+    "Thumbs.db",
+    ".DS_Store",
+]
+
+add_module_names = False
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = "sphinx"
+
+# see http://stackoverflow.com/q/12206334/562769
+numpydoc_show_class_members = True
+# this is needed for some reason...
+# see https://github.com/numpy/numpydoc/issues/69
+numpydoc_class_members_toctree = False
+
+# https://numpydoc.readthedocs.io/en/latest/validation.html#built-in-validation-checks
+# Let's turn off the check for building but keep it in pre-commit hooks
+numpydoc_validation_checks = set()
+
+# generate autosummary even if no references
+autosummary_generate = True
+
+# Members and inherited-members default to showing methods and attributes from a
+# class or those inherited.
+# Member-order orders the documentation in the order of how the members are defined in
+# the source code.
+autodoc_default_options = {
+    "members": True,
+    "inherited-members": True,
+    "member-order": "bysource",
+}
+
+# If true, '()' will be appended to :func: etc. cross-reference text.
+add_function_parentheses = False
+
+# Suppress warnings
+suppress_warnings = [
+    "myst.mathjax",
+    "docutils",
+    "toc.not_included",
+    "autodoc.import_object",
+    "autosectionlabel",
+    "ref",
+]
+
+show_warning_types = True
+
+# Link to GitHub repo for github_issues extension
+issues_github_path = "SimonBlanke/Hyperactive"
+
+
+def linkcode_resolve(domain, info):
+    """Return URL to source code corresponding.
+
+    Parameters
+    ----------
+    domain : str
+    info : dict
+
+    Returns
+    -------
+    url : str
+    """
+
+    def find_source():
+        # try to find the file and line number, based on code from numpy:
+        # https://github.com/numpy/numpy/blob/main/doc/source/conf.py#L286
+        obj = sys.modules[info["module"]]
+        for part in info["fullname"].split("."):
+            obj = getattr(obj, part)
+        import inspect
+        import os
+
+        fn = inspect.getsourcefile(obj)
+        fn = os.path.relpath(fn, start=os.path.dirname(hyperactive.__file__))
+        source, lineno = inspect.getsourcelines(obj)
+        return fn, lineno, lineno + len(source) - 1
+
+    if domain != "py" or not info["module"]:
+        return None
+    try:
+        filename = "src/hyperactive/%s#L%d-L%d" % find_source()
+    except Exception:
+        filename = info["module"].replace(".", "/") + ".py"
+    return (
+        f"https://github.com/SimonBlanke/Hyperactive/blob/{CURRENT_VERSION}/{filename}"
+    )
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+
+html_theme = "pydata_sphinx_theme"
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+
+html_theme_options = {
+    "logo": {
+        "image_light": "_static/images/navbar_logo.svg",
+        "image_dark": "_static/images/navbar_logo_dark.svg",
+    },
+    "icon_links": [
+        {
+            "name": "GitHub",
+            "url": "https://github.com/SimonBlanke/Hyperactive",
+            "icon": "fab fa-github",
+        },
+        {
+            "name": "Star on GitHub",
+            "url": "https://github.com/SimonBlanke/Hyperactive/stargazers",
+            "icon": "fa-regular fa-star",
+        },
+    ],
+    "show_prev_next": False,
+    "use_edit_page_button": False,
+    "navbar_start": ["navbar-logo"],
+    "navbar_center": ["navbar-nav"],
+    "navbar_end": ["theme-switcher", "navbar-icon-links"],
+    "show_toc_level": 2,
+    "secondary_sidebar_items": ["page-toc", "sourcelink"],
+}
+
+html_title = "Hyperactive"
+html_context = {
+    "github_user": "SimonBlanke",
+    "github_repo": "Hyperactive",
+    "github_version": "master",
+    "doc_path": "auto-doc/source/",
+    "python_version_range": _py_version_range,
+}
+
+html_sidebars = {
+    "**": ["sidebar-nav-bs.html"],
+    "index": [],
+    "get_started": [],
+    "installation": [],
+    "search": [],
+}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static"]
+html_css_files = ["css/custom.css"]
+
+html_show_sourcelink = False
+
+# -- Options for HTMLHelp output ---------------------------------------------
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = "hyperactivedoc"
+
+# -- Options for LaTeX output ------------------------------------------------
+
+latex_elements = {
+    # The paper size ('letterpaper' or 'a4paper').
+    # 'papersize': 'letterpaper',
+    # The font size ('10pt', '11pt' or '12pt').
+    # 'pointsize': '10pt',
+    # Additional stuff for the LaTeX preamble.
+    # 'preamble': '',
+    # Latex figure (float) alignment
+    # 'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+    (
+        master_doc,
+        "hyperactive.tex",
+        "Hyperactive Documentation",
+        "Simon Blanke",
+        "manual",
+    ),
+]
+
+# -- Options for manual page output ------------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [(master_doc, "hyperactive", "Hyperactive Documentation", [author], 1)]
+
+# -- Options for Texinfo output ----------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+    (
+        master_doc,
+        "hyperactive",
+        "Hyperactive Documentation",
+        author,
+        "hyperactive",
+        "An optimization and data collection toolbox for convenient and fast prototyping.",
+        "Miscellaneous",
+    ),
+]
+
+
+def setup(app):
+    """Set up sphinx builder.
+
+    Parameters
+    ----------
+    app : Sphinx application object
+    """
+
+    def adds(pth):
+        print("Adding stylesheet: %s" % pth)  # noqa: T201, T001
+        app.add_css_file(pth)
+
+    adds("fields.css")  # for parameters, etc.
+
+
+# -- Extension configuration -------------------------------------------------
+
+# -- Options for intersphinx extension ---------------------------------------
+
+# Example configuration for intersphinx: refer to the Python standard library.
+intersphinx_mapping = {
+    "python": (f"https://docs.python.org/{sys.version_info.major}", None),
+    "numpy": ("https://numpy.org/doc/stable/", None),
+    "scipy": ("https://docs.scipy.org/doc/scipy/", None),
+    "matplotlib": ("https://matplotlib.org/stable/", None),
+    "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None),
+    "joblib": ("https://joblib.readthedocs.io/en/stable/", None),
+    "scikit-learn": ("https://scikit-learn.org/stable/", None),
+}
+
+# -- Options for todo extension ----------------------------------------------
+todo_include_todos = False
+
+copybutton_prompt_text = r">>> |\.\.\. |\$ "
+copybutton_prompt_is_regexp = True
+copybutton_line_continuation_character = "\\"
+
+# -- RST Epilog: Make metadata available as substitutions in RST files -------
+# These can be used as |variable_name| in RST files
+
+# Build additional Python versions formatting for RST
+if _py_versions:
+    _py_versions_inline = ", ".join(_py_versions)
+else:
+    _py_versions_inline = "3.10+"
+
+
+# -- Count algorithms and integrations dynamically ----------------------------
+def count_from_all_list(module_path: str) -> int:
+    """Count items in __all__ list of a Python module file."""
+    import ast
+
+    file_path = Path(__file__).parent.parent.parent / "src" / module_path
+    if not file_path.exists():
+        return 0
+
+    try:
+        tree = ast.parse(file_path.read_text())
+        for node in ast.walk(tree):
+            if isinstance(node, ast.Assign):
+                for target in node.targets:
+                    if isinstance(target, ast.Name) and target.id == "__all__":
+                        if isinstance(node.value, ast.List):
+                            return len(node.value.elts)
+    except Exception:
+        pass
+    return 0
+
+
+# Count algorithms from opt/__init__.py
+_n_algorithms = count_from_all_list("hyperactive/opt/__init__.py")
+
+# Count integrations from experiment/integrations/__init__.py
+_n_integrations = count_from_all_list("hyperactive/experiment/integrations/__init__.py")
+
+# Backends are conceptual (GFO, Optuna, sklearn) - hardcoded
+_n_backends = 3
+
+
+rst_epilog = f"""
+.. |version| replace:: {PYPROJECT_METADATA["version"]}
+.. |min_python| replace:: {PYPROJECT_METADATA["min_python"]}
+.. |python_versions_list| replace:: {_py_versions_inline}
+.. |python_version_range| replace:: {_py_version_range}
+.. |current_year| replace:: {current_year}
+.. |n_algorithms| replace:: {_n_algorithms}
+.. |n_backends| replace:: {_n_backends}
+.. |n_integrations| replace:: {_n_integrations}
+"""
diff --git a/docs/source/examples.rst b/docs/source/examples.rst
new file mode 100644
index 00000000..c8b88c45
--- /dev/null
+++ b/docs/source/examples.rst
@@ -0,0 +1,83 @@
+.. _examples:
+
+========
+Examples
+========
+
+This section provides a collection of examples demonstrating Hyperactive's capabilities.
+All examples are available in the
+`examples directory <https://github.com/SimonBlanke/Hyperactive/tree/master/examples>`_
+on GitHub.
+
+.. toctree::
+   :maxdepth: 1
+
+   examples/general
+   examples/local_search
+   examples/global_search
+   examples/population_based
+   examples/sequential_model_based
+   examples/optuna_backend
+   examples/sklearn_backend
+   examples/integrations
+   examples/other
+   examples/interactive_tutorial
+
+
+Overview
+--------
+
+Hyperactive provides examples for all optimization algorithms and integration
+patterns. The examples are organized by algorithm category:
+
+
+Gradient-Free Optimizers (GFO)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+:ref:`examples_general`
+    Basic examples showing custom function optimization and sklearn model tuning.
+
+:ref:`examples_local_search`
+    Hill Climbing, Simulated Annealing, Downhill Simplex, and other local
+    search methods that explore by making incremental moves.
+
+:ref:`examples_global_search`
+    Random Search, Grid Search, Powell's Method, and other algorithms that
+    explore the search space more broadly.
+
+:ref:`examples_population_based`
+    Particle Swarm, Genetic Algorithm, Evolution Strategy, and other
+    nature-inspired population methods.
+
+:ref:`examples_sequential_model_based`
+    Bayesian Optimization, Tree-Parzen Estimators, and other model-based
+    methods that learn from previous evaluations.
+
+
+Backend Examples
+^^^^^^^^^^^^^^^^
+
+:ref:`examples_optuna_backend`
+    Examples using Optuna's samplers including TPE, CMA-ES, NSGA-II/III,
+    and Gaussian Process optimization.
+
+:ref:`examples_sklearn_backend`
+    Scikit-learn compatible interfaces as drop-in replacements for
+    GridSearchCV and RandomizedSearchCV.
+
+
+Integration Examples
+^^^^^^^^^^^^^^^^^^^^
+
+:ref:`examples_integrations`
+    Time series optimization with sktime and other framework integrations.
+
+
+Advanced Topics
+^^^^^^^^^^^^^^^
+
+:ref:`examples_other`
+    Advanced usage patterns including warm starting and optimizer comparison.
+
+:ref:`examples_interactive_tutorial`
+    Comprehensive Jupyter notebook tutorial covering all Hyperactive features.
diff --git a/docs/source/examples/general.rst b/docs/source/examples/general.rst
new file mode 100644
index 00000000..113171b8
--- /dev/null
+++ b/docs/source/examples/general.rst
@@ -0,0 +1,47 @@
+.. _examples_general:
+
+================
+General Examples
+================
+
+These examples demonstrate Hyperactive's core functionality with simple,
+illustrative use cases.
+
+
+Running Examples
+----------------
+
+All examples are available in the
+`examples directory <https://github.com/SimonBlanke/Hyperactive/tree/master/examples>`_
+on GitHub. You can run any example directly:
+
+.. code-block:: bash
+
+    # Clone the repository
+    git clone https://github.com/SimonBlanke/Hyperactive.git
+    cd Hyperactive/examples
+
+    # Run an example
+    python gfo/hill_climbing_example.py
+
+
+Custom Function Optimization
+----------------------------
+
+The simplest use case: optimizing a mathematical function.
+
+.. literalinclude:: ../_snippets/examples/basic_examples.py
+   :language: python
+   :start-after: # [start:custom_function]
+   :end-before: # [end:custom_function]
+
+
+Scikit-learn Model Tuning
+-------------------------
+
+Hyperparameter optimization for machine learning models.
+
+.. literalinclude:: ../_snippets/examples/basic_examples.py
+   :language: python
+   :start-after: # [start:sklearn_tuning]
+   :end-before: # [end:sklearn_tuning]
diff --git a/docs/source/examples/global_search.rst b/docs/source/examples/global_search.rst
new file mode 100644
index 00000000..d50ec5d5
--- /dev/null
+++ b/docs/source/examples/global_search.rst
@@ -0,0 +1,44 @@
+.. _examples_global_search:
+
+========================
+Global Search Algorithms
+========================
+
+Global search algorithms explore the search space more broadly, using
+randomization or systematic patterns to avoid getting trapped in local optima.
+
+
+Algorithm Examples
+------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Algorithm
+     - Example
+   * - Random Search
+     - `random_search_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/random_search_example.py>`_
+   * - Grid Search
+     - `grid_search_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/grid_search_example.py>`_
+   * - Random Restart Hill Climbing
+     - `random_restart_hill_climbing_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/random_restart_hill_climbing_example.py>`_
+   * - Stochastic Hill Climbing
+     - `stochastic_hill_climbing_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/stochastic_hill_climbing_example.py>`_
+   * - Powell's Method
+     - `powells_method_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/powells_method_example.py>`_
+   * - Pattern Search
+     - `pattern_search_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/pattern_search_example.py>`_
+
+
+When to Use Global Search
+-------------------------
+
+Global search algorithms are best suited for:
+
+- **Multimodal search spaces** with multiple local optima
+- **Initial exploration** before fine-tuning with local search
+- **Unknown search spaces** where the landscape is not well understood
+- **Baseline comparisons** (especially Random Search)
+
+See :ref:`user_guide_optimizers` for detailed algorithm descriptions.
diff --git a/docs/source/examples/integrations.rst b/docs/source/examples/integrations.rst
new file mode 100644
index 00000000..2fff131c
--- /dev/null
+++ b/docs/source/examples/integrations.rst
@@ -0,0 +1,49 @@
+.. _examples_integrations:
+
+============
+Integrations
+============
+
+Hyperactive integrates with popular machine learning frameworks beyond
+scikit-learn, including time series libraries.
+
+
+Sktime Integration
+------------------
+
+For time series forecasting and classification with sktime:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Use Case
+     - Example
+   * - Time Series Forecasting
+     - `sktime_forecasting_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/integrations/sktime_forecasting_example.py>`_
+   * - Time Series Classification
+     - `sktime_tsc_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/integrations/sktime_tsc_example.py>`_
+
+.. note::
+
+   Sktime integration requires additional dependencies:
+
+   .. code-block:: bash
+
+       pip install hyperactive[sktime-integration]
+
+
+Installing Extras
+-----------------
+
+Install integration extras as needed:
+
+.. code-block:: bash
+
+    # Sktime/skpro for time series
+    pip install hyperactive[sktime-integration]
+
+    # All extras including PyTorch Lightning
+    pip install hyperactive[all_extras]
+
+See :ref:`user_guide_integrations` for complete integration documentation.
diff --git a/docs/source/examples/interactive_tutorial.rst b/docs/source/examples/interactive_tutorial.rst
new file mode 100644
index 00000000..b4e63e75
--- /dev/null
+++ b/docs/source/examples/interactive_tutorial.rst
@@ -0,0 +1,50 @@
+.. _examples_interactive_tutorial:
+
+====================
+Interactive Tutorial
+====================
+
+For hands-on learning, we provide a comprehensive Jupyter notebook tutorial
+that covers all aspects of Hyperactive.
+
+
+Tutorial Notebook
+-----------------
+
+`Hyperactive Tutorial Notebook <https://nbviewer.org/github/SimonBlanke/hyperactive-tutorial/blob/main/notebooks/hyperactive_tutorial.ipynb>`_
+
+This interactive notebook covers:
+
+- **Basic optimization concepts** — Understanding search spaces and objective functions
+- **All optimizer categories** — Hands-on examples with each algorithm type
+- **Real-world ML examples** — Practical hyperparameter optimization workflows
+- **Best practices and tips** — Common pitfalls and how to avoid them
+
+
+Running the Tutorial
+--------------------
+
+You can run the tutorial locally:
+
+.. code-block:: bash
+
+    # Clone the tutorial repository
+    git clone https://github.com/SimonBlanke/hyperactive-tutorial.git
+    cd hyperactive-tutorial
+
+    # Install dependencies
+    pip install -r requirements.txt
+
+    # Launch Jupyter
+    jupyter notebook notebooks/hyperactive_tutorial.ipynb
+
+Or view it directly on `nbviewer <https://nbviewer.org/github/SimonBlanke/hyperactive-tutorial/blob/main/notebooks/hyperactive_tutorial.ipynb>`_
+without any installation.
+
+
+Additional Resources
+--------------------
+
+- `Hyperactive GitHub Repository <https://github.com/SimonBlanke/Hyperactive>`_
+- `Gradient-Free-Optimizers <https://github.com/SimonBlanke/Gradient-Free-Optimizers>`_ — The underlying optimization library
+- :ref:`user_guide` — Detailed documentation of all features
diff --git a/docs/source/examples/local_search.rst b/docs/source/examples/local_search.rst
new file mode 100644
index 00000000..d4c9ad7a
--- /dev/null
+++ b/docs/source/examples/local_search.rst
@@ -0,0 +1,41 @@
+.. _examples_local_search:
+
+=======================
+Local Search Algorithms
+=======================
+
+Local search algorithms explore the search space by making small, incremental
+moves from the current position. They are efficient for finding local optima
+but may get stuck without escaping mechanisms.
+
+
+Algorithm Examples
+------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Algorithm
+     - Example
+   * - Hill Climbing
+     - `hill_climbing_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/hill_climbing_example.py>`_
+   * - Repulsing Hill Climbing
+     - `repulsing_hill_climbing_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/repulsing_hill_climbing_example.py>`_
+   * - Simulated Annealing
+     - `simulated_annealing_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/simulated_annealing_example.py>`_
+   * - Downhill Simplex
+     - `downhill_simplex_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/downhill_simplex_example.py>`_
+
+
+When to Use Local Search
+------------------------
+
+Local search algorithms are best suited for:
+
+- **Smooth search spaces** where nearby points have similar scores
+- **Fine-tuning** around a known good region
+- **Fast convergence** when a good starting point is available
+- **Limited computational budget** where few evaluations are possible
+
+See :ref:`user_guide_optimizers` for detailed algorithm descriptions.
diff --git a/docs/source/examples/optuna_backend.rst b/docs/source/examples/optuna_backend.rst
new file mode 100644
index 00000000..3a8067b7
--- /dev/null
+++ b/docs/source/examples/optuna_backend.rst
@@ -0,0 +1,56 @@
+.. _examples_optuna_backend:
+
+==============
+Optuna Backend
+==============
+
+Hyperactive provides wrappers for Optuna's optimization algorithms, allowing
+you to use Optuna's powerful samplers with Hyperactive's interface.
+
+.. note::
+
+   Optuna must be installed separately:
+
+   .. code-block:: bash
+
+       pip install optuna
+       # or
+       pip install hyperactive[all_extras]
+
+
+Sampler Examples
+----------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Sampler
+     - Example
+   * - TPE (Tree-Parzen Estimator)
+     - `tpe_sampler_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/optuna/tpe_sampler_example.py>`_
+   * - CMA-ES
+     - `cmaes_sampler_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/optuna/cmaes_sampler_example.py>`_
+   * - Gaussian Process
+     - `gp_sampler_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/optuna/gp_sampler_example.py>`_
+   * - NSGA-II
+     - `nsga_ii_sampler_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/optuna/nsga_ii_sampler_example.py>`_
+   * - NSGA-III
+     - `nsga_iii_sampler_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/optuna/nsga_iii_sampler_example.py>`_
+   * - QMC (Quasi-Monte Carlo)
+     - `qmc_sampler_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/optuna/qmc_sampler_example.py>`_
+   * - Random
+     - `random_sampler_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/optuna/random_sampler_example.py>`_
+   * - Grid
+     - `grid_sampler_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/optuna/grid_sampler_example.py>`_
+
+
+When to Use Optuna Backend
+--------------------------
+
+The Optuna backend is useful when you need:
+
+- **Multi-objective optimization** (NSGA-II, NSGA-III)
+- **Advanced sampling strategies** like CMA-ES or QMC
+- **Optuna's pruning capabilities** for early stopping
+- **Compatibility** with existing Optuna workflows
diff --git a/docs/source/examples/other.rst b/docs/source/examples/other.rst
new file mode 100644
index 00000000..ce96ca09
--- /dev/null
+++ b/docs/source/examples/other.rst
@@ -0,0 +1,46 @@
+.. _examples_other:
+
+=================
+Advanced Examples
+=================
+
+These examples demonstrate advanced Hyperactive features for more sophisticated
+optimization workflows.
+
+
+Warm Starting Optimization
+--------------------------
+
+Start optimization from known good points to accelerate convergence:
+
+.. literalinclude:: ../_snippets/examples/advanced_examples.py
+   :language: python
+   :start-after: # [start:warm_starting]
+   :end-before: # [end:warm_starting]
+
+
+Comparing Optimizers
+--------------------
+
+Compare different optimization strategies on the same problem:
+
+.. literalinclude:: ../_snippets/examples/advanced_examples.py
+   :language: python
+   :start-after: # [start:comparing_optimizers]
+   :end-before: # [end:comparing_optimizers]
+
+
+Tips for Advanced Usage
+-----------------------
+
+**Warm Starting**
+
+- Use results from previous runs to seed new optimizations
+- Helpful when iterating on model architecture or features
+- Combine with local search for fine-tuning around known good points
+
+**Optimizer Comparison**
+
+- Always use the same ``random_state`` for reproducible comparisons
+- Run multiple trials to account for optimizer randomness
+- Consider both final score and convergence speed
diff --git a/docs/source/examples/population_based.rst b/docs/source/examples/population_based.rst
new file mode 100644
index 00000000..f115457f
--- /dev/null
+++ b/docs/source/examples/population_based.rst
@@ -0,0 +1,45 @@
+.. _examples_population_based:
+
+===========================
+Population-Based Algorithms
+===========================
+
+Population-based algorithms maintain multiple candidate solutions simultaneously,
+using mechanisms inspired by natural evolution or swarm behavior to explore
+the search space efficiently.
+
+
+Algorithm Examples
+------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Algorithm
+     - Example
+   * - Particle Swarm
+     - `particle_swarm_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/particle_swarm_example.py>`_
+   * - Genetic Algorithm
+     - `genetic_algorithm_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/genetic_algorithm_example.py>`_
+   * - Evolution Strategy
+     - `evolution_strategy_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/evolution_strategy_example.py>`_
+   * - Differential Evolution
+     - `differential_evolution_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/differential_evolution_example.py>`_
+   * - Parallel Tempering
+     - `parallel_tempering_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/parallel_tempering_example.py>`_
+   * - Spiral Optimization
+     - `spiral_optimization_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/spiral_optimization_example.py>`_
+
+
+When to Use Population-Based Methods
+------------------------------------
+
+Population-based algorithms are best suited for:
+
+- **Complex, multimodal landscapes** with many local optima
+- **Parallelizable evaluations** where multiple candidates can be evaluated simultaneously
+- **Robust optimization** where diversity helps avoid premature convergence
+- **Large search spaces** requiring extensive exploration
+
+See :ref:`user_guide_optimizers` for detailed algorithm descriptions.
diff --git a/docs/source/examples/sequential_model_based.rst b/docs/source/examples/sequential_model_based.rst
new file mode 100644
index 00000000..bde4fca2
--- /dev/null
+++ b/docs/source/examples/sequential_model_based.rst
@@ -0,0 +1,43 @@
+.. _examples_sequential_model_based:
+
+=================================
+Sequential Model-Based Algorithms
+=================================
+
+Sequential model-based optimization (SMBO) algorithms build a surrogate model
+of the objective function to guide the search. They are particularly effective
+when function evaluations are expensive.
+
+
+Algorithm Examples
+------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Algorithm
+     - Example
+   * - Bayesian Optimization
+     - `bayesian_optimization_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/bayesian_optimization_example.py>`_
+   * - Tree-Parzen Estimators
+     - `tree_structured_parzen_estimators_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/tree_structured_parzen_estimators_example.py>`_
+   * - Forest Optimizer
+     - `forest_optimizer_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/forest_optimizer_example.py>`_
+   * - Lipschitz Optimizer
+     - `lipschitz_optimizer_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/lipschitz_optimizer_example.py>`_
+   * - DIRECT Algorithm
+     - `direct_algorithm_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/gfo/direct_algorithm_example.py>`_
+
+
+When to Use Model-Based Methods
+-------------------------------
+
+Sequential model-based algorithms are best suited for:
+
+- **Expensive objective functions** (e.g., training neural networks, simulations)
+- **Limited evaluation budgets** where each evaluation counts
+- **Smooth, continuous search spaces** where surrogate models work well
+- **Hyperparameter optimization** for machine learning models
+
+See :ref:`user_guide_optimizers` for detailed algorithm descriptions.
diff --git a/docs/source/examples/sklearn_backend.rst b/docs/source/examples/sklearn_backend.rst
new file mode 100644
index 00000000..b74f0623
--- /dev/null
+++ b/docs/source/examples/sklearn_backend.rst
@@ -0,0 +1,57 @@
+.. _examples_sklearn_backend:
+
+===============
+Sklearn Backend
+===============
+
+Hyperactive provides scikit-learn compatible interfaces that work as drop-in
+replacements for ``GridSearchCV`` and ``RandomizedSearchCV``.
+
+
+Example Files
+-------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Use Case
+     - Example
+   * - Classification with OptCV
+     - `sklearn_classif_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/integrations/sklearn_classif_example.py>`_
+   * - Grid Search
+     - `grid_search_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/sklearn/grid_search_example.py>`_
+   * - Random Search
+     - `random_search_example.py <https://github.com/SimonBlanke/Hyperactive/blob/master/examples/sklearn/random_search_example.py>`_
+
+
+Usage Overview
+--------------
+
+Hyperactive's sklearn-compatible classes follow the familiar fit/predict pattern:
+
+.. code-block:: python
+
+    from hyperactive.integrations.sklearn import HyperactiveSearchCV
+    from sklearn.ensemble import RandomForestClassifier
+
+    # Define search space
+    param_space = {
+        "n_estimators": [50, 100, 200],
+        "max_depth": [5, 10, 15, None],
+    }
+
+    # Create search object
+    search = HyperactiveSearchCV(
+        estimator=RandomForestClassifier(),
+        param_space=param_space,
+        n_iter=50,
+    )
+
+    # Fit like any sklearn estimator
+    search.fit(X_train, y_train)
+
+    # Access best parameters
+    print(search.best_params_)
+
+See :ref:`user_guide_integrations` for complete documentation.
diff --git a/docs/source/faq.rst b/docs/source/faq.rst
new file mode 100644
index 00000000..ed5360a9
--- /dev/null
+++ b/docs/source/faq.rst
@@ -0,0 +1,40 @@
+.. _faq:
+
+==========================
+Frequently Asked Questions
+==========================
+
+This section answers common questions about Hyperactive. For migration from v4,
+see the :ref:`user_guide_migration`.
+
+.. toctree::
+   :maxdepth: 1
+
+   faq/getting_started
+   faq/search_space
+   faq/common_issues
+   faq/advanced_usage
+   faq/integrations
+   faq/getting_help
+
+
+Overview
+--------
+
+:ref:`faq_getting_started`
+    Choosing optimizers, iteration counts, and understanding maximization.
+
+:ref:`faq_search_space`
+    Defining continuous, discrete, and mixed parameter spaces.
+
+:ref:`faq_common_issues`
+    Slow optimization, reproducibility, handling errors.
+
+:ref:`faq_advanced_usage`
+    Parallel execution, callbacks, parameter constraints.
+
+:ref:`faq_integrations`
+    Using Hyperactive with PyTorch, XGBoost, and other frameworks.
+
+:ref:`faq_getting_help`
+    Where to report bugs and get support.
diff --git a/docs/source/faq/advanced_usage.rst b/docs/source/faq/advanced_usage.rst
new file mode 100644
index 00000000..2199cc38
--- /dev/null
+++ b/docs/source/faq/advanced_usage.rst
@@ -0,0 +1,69 @@
+.. _faq_advanced_usage:
+
+==============
+Advanced Usage
+==============
+
+Can I run optimizations in parallel?
+------------------------------------
+
+Currently, Hyperactive v5 runs single optimizer instances.
+For parallel evaluation of candidates, consider using Optuna
+backend optimizers which support parallel trials:
+
+.. code-block:: python
+
+    from hyperactive.opt.optuna import TPEOptimizer
+
+    optimizer = TPEOptimizer(
+        search_space=search_space,
+        n_iter=100,
+        experiment=objective,
+        # Optuna handles parallelization
+    )
+
+
+Can I save and resume optimization?
+-----------------------------------
+
+This feature is planned but not yet available in v5. As a workaround,
+you can log results during optimization and use them as initial points
+for a new run.
+
+
+Are callbacks supported?
+------------------------
+
+User-defined callbacks during optimization are not currently supported in v5.
+The Optuna backend has internal early-stopping callbacks, but there's no
+general callback interface for tracking progress or modifying behavior during
+optimization.
+
+For progress monitoring, you can add logging inside your objective function:
+
+.. code-block:: python
+
+    iteration = 0
+
+    def objective(params):
+        global iteration
+        iteration += 1
+        score = evaluate_model(params)
+        print(f"Iteration {iteration}: score={score:.4f}")
+        return score
+
+
+How do I add constraints between parameters?
+--------------------------------------------
+
+Handle constraints in your objective function by returning a poor score
+for invalid combinations:
+
+.. code-block:: python
+
+    def objective(params):
+        # Constraint: min_samples_split must be >= min_samples_leaf
+        if params["min_samples_split"] < params["min_samples_leaf"]:
+            return -np.inf  # Invalid configuration
+
+        return evaluate_model(params)
diff --git a/docs/source/faq/common_issues.rst b/docs/source/faq/common_issues.rst
new file mode 100644
index 00000000..e95f3466
--- /dev/null
+++ b/docs/source/faq/common_issues.rst
@@ -0,0 +1,72 @@
+.. _faq_common_issues:
+
+=============
+Common Issues
+=============
+
+Why is my optimization slow?
+----------------------------
+
+**Slow objective function**: The optimizer only controls search strategy.
+If each evaluation takes a long time, consider:
+
+- Reducing cross-validation folds
+- Using a subset of training data for tuning
+- Simplifying your model during search
+
+**Large search space**: More combinations require more iterations.
+Consider reducing parameter granularity or using smarter optimizers
+like Bayesian optimization.
+
+**Too many iterations**: Start with fewer iterations and increase
+if needed.
+
+
+Why does my score vary between runs?
+------------------------------------
+
+Optimization algorithms are stochastic. To get reproducible results,
+set a random seed:
+
+.. code-block:: python
+
+    optimizer = HillClimbing(
+        search_space=search_space,
+        n_iter=100,
+        experiment=objective,
+        random_state=42,  # Set seed for reproducibility
+    )
+
+
+My objective function returns NaN or raises exceptions
+------------------------------------------------------
+
+Handle invalid configurations in your objective function:
+
+.. code-block:: python
+
+    def objective(params):
+        try:
+            score = evaluate_model(params)
+            if np.isnan(score):
+                return -np.inf  # Return worst possible score
+            return score
+        except Exception:
+            return -np.inf  # Return worst possible score on error
+
+
+How do I see what parameters were tried?
+----------------------------------------
+
+Access the search history after optimization:
+
+.. code-block:: python
+
+    best_params = optimizer.solve()
+
+    # Access results
+    print(f"Best parameters: {optimizer.best_params_}")
+    print(f"Best score: {optimizer.best_score_}")
+
+    # Full search history (if available)
+    # Check optimizer attributes for search_data or similar
diff --git a/docs/source/faq/getting_help.rst b/docs/source/faq/getting_help.rst
new file mode 100644
index 00000000..acb918cb
--- /dev/null
+++ b/docs/source/faq/getting_help.rst
@@ -0,0 +1,28 @@
+.. _faq_getting_help:
+
+============
+Getting Help
+============
+
+Where can I report bugs or request features?
+--------------------------------------------
+
+Open an issue on `GitHub <https://github.com/SimonBlanke/Hyperactive/issues>`_.
+
+
+Where can I get help?
+---------------------
+
+- Check the :ref:`examples` for code samples
+- Read the :ref:`user_guide` for detailed explanations
+- Join the `Discord <https://discord.gg/7uKdHfdcJG>`_ community
+- Search or ask on `GitHub Discussions <https://github.com/SimonBlanke/Hyperactive/discussions>`_
+
+
+Where is the documentation for older versions?
+----------------------------------------------
+
+Documentation for Hyperactive v4 is available at:
+`Legacy Documentation (v4) <https://simonblanke.github.io/hyperactive-documentation/5.0/>`_
+
+If you're migrating from v4 to v5, see the :ref:`user_guide_migration`.
diff --git a/docs/source/faq/getting_started.rst b/docs/source/faq/getting_started.rst
new file mode 100644
index 00000000..fbb3f01d
--- /dev/null
+++ b/docs/source/faq/getting_started.rst
@@ -0,0 +1,53 @@
+.. _faq_getting_started:
+
+===============
+Getting Started
+===============
+
+Which optimizer should I use?
+-----------------------------
+
+For most problems, start with one of these recommendations:
+
+**Small search spaces (<100 combinations)**
+   Use :class:`~hyperactive.opt.gfo.GridSearch` to exhaustively evaluate all options.
+
+**General-purpose optimization**
+   :class:`~hyperactive.opt.gfo.BayesianOptimizer` works well for expensive
+   objective functions where you want to minimize evaluations.
+
+**Fast, simple problems**
+   :class:`~hyperactive.opt.gfo.HillClimbing` or
+   :class:`~hyperactive.opt.gfo.RandomSearch` are good starting points.
+
+**High-dimensional spaces**
+   Population-based methods like :class:`~hyperactive.opt.gfo.ParticleSwarmOptimizer`
+   or :class:`~hyperactive.opt.gfo.EvolutionStrategyOptimizer` handle many
+   parameters well.
+
+See :ref:`user_guide_optimizers` for detailed guidance on choosing optimizers.
+
+
+How many iterations do I need?
+------------------------------
+
+This depends on your search space size and objective function:
+
+- **Rule of thumb**: Start with ``n_iter = 10 * number_of_parameters``
+- **Expensive functions**: Use fewer iterations with Bayesian optimization
+- **Fast functions**: Use more iterations with simpler optimizers
+
+You can monitor progress and stop early if the score plateaus.
+
+
+Does Hyperactive minimize or maximize?
+--------------------------------------
+
+**Hyperactive maximizes** the objective function. If you want to minimize,
+return the negative of your metric:
+
+.. code-block:: python
+
+    def objective(params):
+        error = compute_error(params)
+        return -error  # Negate to minimize
diff --git a/docs/source/faq/integrations.rst b/docs/source/faq/integrations.rst
new file mode 100644
index 00000000..c6d693b6
--- /dev/null
+++ b/docs/source/faq/integrations.rst
@@ -0,0 +1,64 @@
+.. _faq_integrations:
+
+============
+Integrations
+============
+
+Can I use Hyperactive with PyTorch (not Lightning)?
+---------------------------------------------------
+
+Yes, create a custom objective function:
+
+.. code-block:: python
+
+    import torch
+
+    def objective(params):
+        model = MyPyTorchModel(
+            hidden_size=params["hidden_size"],
+            dropout=params["dropout"],
+        )
+        # Train and evaluate your model
+        train_model(model, train_loader)
+        accuracy = evaluate_model(model, val_loader)
+        return accuracy
+
+
+How does Hyperactive compare to Optuna?
+---------------------------------------
+
+**Hyperactive with native GFO backend**:
+
+- Simple, unified API
+- Wide variety of optimization algorithms
+- Great for hyperparameter tuning
+
+**Hyperactive with Optuna backend**:
+
+- Access Optuna's algorithms through Hyperactive's interface
+- Combine the strengths of both libraries
+
+**Pure Optuna**:
+
+- More features (pruning, distributed, database storage)
+- Larger community and ecosystem
+- More configuration options
+
+Choose based on your needs: Hyperactive for simplicity, Optuna for
+advanced features.
+
+
+Can I use Hyperactive with other ML frameworks?
+-----------------------------------------------
+
+Yes, any framework works with custom objective functions:
+
+.. code-block:: python
+
+    # XGBoost example
+    import xgboost as xgb
+
+    def objective(params):
+        model = xgb.XGBClassifier(**params)
+        scores = cross_val_score(model, X, y, cv=3)
+        return scores.mean()
diff --git a/docs/source/faq/search_space.rst b/docs/source/faq/search_space.rst
new file mode 100644
index 00000000..eefccc36
--- /dev/null
+++ b/docs/source/faq/search_space.rst
@@ -0,0 +1,49 @@
+.. _faq_search_space:
+
+======================
+Search Space Questions
+======================
+
+How do I define a continuous search space?
+------------------------------------------
+
+Use NumPy to create arrays of values:
+
+.. code-block:: python
+
+    import numpy as np
+
+    search_space = {
+        "learning_rate": np.logspace(-4, -1, 50),  # 0.0001 to 0.1
+        "momentum": np.linspace(0.5, 0.99, 50),    # 0.5 to 0.99
+    }
+
+Hyperactive samples from these arrays, so finer granularity gives
+more precision at the cost of a larger search space.
+
+
+Can I mix discrete and continuous parameters?
+---------------------------------------------
+
+Yes, mix freely:
+
+.. code-block:: python
+
+    search_space = {
+        "n_estimators": [10, 50, 100, 200],          # Discrete
+        "max_depth": list(range(3, 20)),             # Discrete range
+        "learning_rate": np.linspace(0.01, 0.3, 30), # Continuous
+        "algorithm": ["SAMME", "SAMME.R"],           # Categorical
+    }
+
+
+How do I include None as a parameter value?
+-------------------------------------------
+
+Include ``None`` directly in your list:
+
+.. code-block:: python
+
+    search_space = {
+        "max_depth": [None, 3, 5, 10, 20],
+    }
diff --git a/docs/source/get_involved.rst b/docs/source/get_involved.rst
new file mode 100644
index 00000000..a7ada9ac
--- /dev/null
+++ b/docs/source/get_involved.rst
@@ -0,0 +1,123 @@
+.. _get_involved:
+
+============
+Get Involved
+============
+
+Hyperactive is an open-source project and we welcome contributions from the community!
+There are many ways to get involved, whether you're a developer, researcher, or user.
+
+.. toctree::
+   :maxdepth: 1
+
+   get_involved/contributing
+   get_involved/code_of_conduct
+
+
+Ways to Contribute
+------------------
+
+Report Bugs
+^^^^^^^^^^^
+
+Found a bug? Please report it on GitHub:
+
+1. Search `existing issues <https://github.com/SimonBlanke/Hyperactive/issues>`_
+   to see if it's already reported.
+2. If not, `open a new issue <https://github.com/SimonBlanke/Hyperactive/issues/new>`_
+   with:
+
+   - A clear description of the problem
+   - Steps to reproduce the issue
+   - Expected vs actual behavior
+   - Your Python version and Hyperactive version
+   - Minimal code example that reproduces the issue
+
+Suggest Features
+^^^^^^^^^^^^^^^^
+
+Have an idea for a new feature or improvement?
+
+1. Open a `GitHub Discussion <https://github.com/SimonBlanke/Hyperactive/discussions>`_
+   to discuss your idea with the community.
+2. If there's consensus, create an issue or pull request.
+
+Contribute Code
+^^^^^^^^^^^^^^^
+
+Ready to contribute code? See the :ref:`contributing` guide for:
+
+- Setting up your development environment
+- Coding standards and style guide
+- How to submit pull requests
+- Testing requirements
+
+Improve Documentation
+^^^^^^^^^^^^^^^^^^^^^
+
+Documentation improvements are always welcome:
+
+- Fix typos or unclear explanations
+- Add examples and tutorials
+- Improve API documentation
+- Translate documentation
+
+Share Your Work
+^^^^^^^^^^^^^^^
+
+Using Hyperactive in your project? Share your experience:
+
+- Write a blog post or tutorial
+- Present at a meetup or conference
+- Share on social media
+- Add your project to the examples
+
+
+Community
+---------
+
+Connect with the Hyperactive community:
+
+Discord
+^^^^^^^
+
+Join our `Discord server <https://discord.gg/7uKdHfdcJG>`_ for:
+
+- Real-time discussions
+- Questions and answers
+- Announcements
+
+GitHub Discussions
+^^^^^^^^^^^^^^^^^^
+
+Use `GitHub Discussions <https://github.com/SimonBlanke/Hyperactive/discussions>`_ for:
+
+- Feature proposals
+- Best practices
+- Show and tell
+
+LinkedIn
+^^^^^^^^
+
+Follow the `German Center for Open Source AI <https://www.linkedin.com/company/german-center-for-open-source-ai>`_
+for news and updates.
+
+
+Support
+-------
+
+Getting Help
+^^^^^^^^^^^^
+
+If you need help:
+
+1. Check the :ref:`user_guide` and :ref:`api_reference`
+2. Search `existing issues <https://github.com/SimonBlanke/Hyperactive/issues>`_
+3. Ask on `Discord <https://discord.gg/7uKdHfdcJG>`_
+4. Open a new issue with the "question" label
+
+Star the Project
+^^^^^^^^^^^^^^^^
+
+If you find Hyperactive useful, please `star it on GitHub <https://github.com/SimonBlanke/Hyperactive>`_!
+Stars help increase visibility and attract more contributors.
diff --git a/docs/source/get_involved/code_of_conduct.rst b/docs/source/get_involved/code_of_conduct.rst
new file mode 100644
index 00000000..ce13223e
--- /dev/null
+++ b/docs/source/get_involved/code_of_conduct.rst
@@ -0,0 +1,114 @@
+.. _code_of_conduct:
+
+===============
+Code of Conduct
+===============
+
+Hyperactive is committed to providing a welcoming and inclusive environment for everyone.
+This Code of Conduct outlines our expectations for participant behavior and the
+consequences for unacceptable behavior.
+
+
+Our Pledge
+----------
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+
+Community Guidelines
+--------------------
+
+Expected Behavior
+^^^^^^^^^^^^^^^^^
+
+We expect all community members to:
+
+- **Be respectful**: Treat everyone with respect and consideration.
+- **Be constructive**: Provide helpful feedback and suggestions.
+- **Be inclusive**: Welcome newcomers and help them get started.
+- **Be collaborative**: Work together toward common goals.
+- **Be patient**: Remember that people have different skill levels and backgrounds.
+- **Be professional**: Keep discussions focused on the project.
+
+
+Unacceptable Behavior
+^^^^^^^^^^^^^^^^^^^^^
+
+The following behaviors are not acceptable:
+
+- Harassment, intimidation, or discrimination of any kind
+- Offensive comments related to personal characteristics
+- Sexual language or imagery in any community space
+- Personal attacks or insults
+- Trolling or deliberately inflammatory comments
+- Publishing others' private information without permission
+- Other conduct that could reasonably be considered inappropriate
+
+
+Enforcement
+-----------
+
+Reporting Issues
+^^^^^^^^^^^^^^^^
+
+If you experience or witness unacceptable behavior, please report it by:
+
+- **Email**: Contact Simon Blanke at simon.blanke@yahoo.com
+- **GitHub**: Open a private issue or contact maintainers directly
+
+All complaints will be reviewed and investigated promptly and fairly.
+
+
+Consequences
+^^^^^^^^^^^^
+
+Community leaders will determine appropriate action for violations, which may include:
+
+1. **Correction**: A private, written warning with clarity about the nature of
+   the violation and an explanation of why the behavior was inappropriate.
+
+2. **Warning**: A warning with consequences for continued behavior. No interaction
+   with the people involved for a specified period of time.
+
+3. **Temporary Ban**: A temporary ban from any sort of interaction or public
+   communication with the community for a specified period of time.
+
+4. **Permanent Ban**: A permanent ban from any sort of public interaction within
+   the community.
+
+
+Scope
+-----
+
+This Code of Conduct applies within all community spaces, including:
+
+- GitHub repositories (issues, pull requests, discussions)
+- Discord server
+- Social media interactions
+- In-person events
+
+It also applies when an individual is officially representing the community
+in public spaces.
+
+
+Attribution
+-----------
+
+This Code of Conduct is adapted from the
+`Contributor Covenant <https://www.contributor-covenant.org/>`_, version 2.0.
+
+
+Questions
+---------
+
+If you have questions about this Code of Conduct, please contact:
+
+- **Email**: simon.blanke@yahoo.com
+- **GitHub**: `Open a discussion <https://github.com/SimonBlanke/Hyperactive/discussions>`_
diff --git a/docs/source/get_involved/contributing.rst b/docs/source/get_involved/contributing.rst
new file mode 100644
index 00000000..f8282f1c
--- /dev/null
+++ b/docs/source/get_involved/contributing.rst
@@ -0,0 +1,250 @@
+.. _contributing:
+
+============
+Contributing
+============
+
+Thank you for your interest in contributing to Hyperactive! This guide will help
+you get started with development and submit your contributions.
+
+
+How to Contribute
+-----------------
+
+Contribution Workflow
+^^^^^^^^^^^^^^^^^^^^^
+
+1. **Fork the repository** on GitHub
+2. **Clone your fork** locally
+3. **Create a branch** for your changes
+4. **Make your changes** with tests
+5. **Run the test suite** to ensure everything works
+6. **Submit a pull request** for review
+
+
+Types of Contributions
+^^^^^^^^^^^^^^^^^^^^^^
+
+We welcome many types of contributions:
+
+- **Bug fixes**: Fix issues and improve stability
+- **New features**: Add new optimizers, experiments, or integrations
+- **Documentation**: Improve guides, examples, and API docs
+- **Tests**: Increase test coverage
+- **Performance**: Optimize code for speed or memory
+
+
+Development Setup
+-----------------
+
+Prerequisites
+^^^^^^^^^^^^^
+
+- Python 3.10 or higher
+- Git
+- pip
+
+Setting Up Your Environment
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+1. Fork and clone the repository:
+
+   .. code-block:: bash
+
+       git clone https://github.com/YOUR-USERNAME/Hyperactive.git
+       cd Hyperactive
+
+2. Create a virtual environment:
+
+   .. code-block:: bash
+
+       python -m venv venv
+       source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+3. Install in development mode with test dependencies:
+
+   .. code-block:: bash
+
+       pip install -e ".[test,docs]"
+
+4. Verify the installation:
+
+   .. code-block:: bash
+
+       python -c "import hyperactive; print(hyperactive.__version__)"
+
+
+Running Tests
+^^^^^^^^^^^^^
+
+Run the test suite to ensure everything works:
+
+.. code-block:: bash
+
+    # Run all tests
+    pytest
+
+    # Run with coverage report
+    pytest --cov=hyperactive
+
+    # Run specific test file
+    pytest tests/test_specific.py
+
+    # Run tests matching a pattern
+    pytest -k "test_hill_climbing"
+
+
+Code Style
+----------
+
+Formatting
+^^^^^^^^^^
+
+Hyperactive uses `Black <https://black.readthedocs.io/>`_ for code formatting
+and `Ruff <https://docs.astral.sh/ruff/>`_ for linting:
+
+.. code-block:: bash
+
+    # Format code
+    black src/hyperactive tests
+
+    # Check linting
+    ruff check src/hyperactive tests
+
+    # Auto-fix linting issues
+    ruff check --fix src/hyperactive tests
+
+
+Docstrings
+^^^^^^^^^^
+
+Use NumPy-style docstrings for all public functions and classes:
+
+.. code-block:: python
+
+    def my_function(param1, param2):
+        """Short description of the function.
+
+        Longer description if needed.
+
+        Parameters
+        ----------
+        param1 : type
+            Description of param1.
+        param2 : type
+            Description of param2.
+
+        Returns
+        -------
+        type
+            Description of return value.
+
+        Examples
+        --------
+        >>> my_function(1, 2)
+        3
+        """
+        return param1 + param2
+
+
+Type Hints
+^^^^^^^^^^
+
+Add type hints to function signatures:
+
+.. code-block:: python
+
+    def optimize(
+        self,
+        search_space: dict,
+        n_iter: int,
+        experiment: Callable,
+    ) -> dict:
+        ...
+
+
+Submitting Changes
+------------------
+
+Creating a Pull Request
+^^^^^^^^^^^^^^^^^^^^^^^
+
+1. **Create a branch** for your changes:
+
+   .. code-block:: bash
+
+       git checkout -b feature/my-new-feature
+
+2. **Make your changes** and commit:
+
+   .. code-block:: bash
+
+       git add .
+       git commit -m "Add my new feature"
+
+3. **Push to your fork**:
+
+   .. code-block:: bash
+
+       git push origin feature/my-new-feature
+
+4. **Open a pull request** on GitHub from your branch to the main repository.
+
+
+Pull Request Guidelines
+^^^^^^^^^^^^^^^^^^^^^^^
+
+- **Clear title**: Describe what the PR does
+- **Description**: Explain the changes and motivation
+- **Tests**: Include tests for new functionality
+- **Documentation**: Update docs if needed
+- **Small scope**: Keep PRs focused on one thing
+
+
+Commit Messages
+^^^^^^^^^^^^^^^
+
+Write clear, descriptive commit messages:
+
+.. code-block:: text
+
+    Add Bayesian optimizer warm start support
+
+    - Add warm_start parameter to BayesianOptimizer
+    - Update documentation with usage examples
+    - Add tests for warm start functionality
+
+
+Review Process
+--------------
+
+What to Expect
+^^^^^^^^^^^^^^
+
+1. **Automated checks**: CI will run tests and linting
+2. **Code review**: Maintainers will review your code
+3. **Feedback**: You may be asked to make changes
+4. **Merge**: Once approved, your PR will be merged
+
+
+Response Time
+^^^^^^^^^^^^^
+
+Maintainers are volunteers, so response times may vary. We aim to:
+
+- Acknowledge PRs within a few days
+- Provide initial review within a week
+- Merge approved PRs promptly
+
+
+Getting Help
+------------
+
+If you need help:
+
+- Check existing `issues <https://github.com/SimonBlanke/Hyperactive/issues>`_
+  and `discussions <https://github.com/SimonBlanke/Hyperactive/discussions>`_
+- Ask on `Discord <https://discord.gg/7uKdHfdcJG>`_
+- Tag @SimonBlanke in your PR for attention
+
+Thank you for contributing to Hyperactive!
diff --git a/docs/source/get_started.rst b/docs/source/get_started.rst
new file mode 100644
index 00000000..1bb1ac1f
--- /dev/null
+++ b/docs/source/get_started.rst
@@ -0,0 +1,111 @@
+.. _get_started:
+
+===========
+Get Started
+===========
+
+This guide will help you get up and running with Hyperactive in just a few minutes.
+By the end, you'll understand the core concepts and be able to run your first optimization.
+
+Quick Start
+-----------
+
+Hyperactive makes hyperparameter optimization simple. Here's a complete example
+that optimizes a custom function:
+
+.. literalinclude:: _snippets/getting_started/quick_start.py
+   :language: python
+   :start-after: # [start:full_example]
+   :end-before: # [end:full_example]
+
+That's it! Let's break down what happened:
+
+1. **Objective function**: A callable that takes a dictionary of parameters and returns a score.
+   Hyperactive **maximizes** this score by default.
+
+2. **Search space**: A dictionary mapping parameter names to their possible values.
+   Use NumPy arrays or lists to define discrete search spaces.
+
+3. **Optimizer**: Choose from 20+ optimization algorithms. Each optimizer explores the
+   search space differently to find optimal parameters.
+
+
+First Steps
+-----------
+
+Optimizing a Scikit-learn Model
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The most common use case is tuning machine learning models. Here's how to optimize
+a Random Forest classifier:
+
+.. literalinclude:: _snippets/getting_started/sklearn_random_forest.py
+   :language: python
+   :start-after: # [start:full_example]
+   :end-before: # [end:full_example]
+
+
+Using the Sklearn Integration
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+For even simpler sklearn integration, use the ``OptCV`` wrapper that behaves like
+scikit-learn's ``GridSearchCV``:
+
+.. literalinclude:: _snippets/getting_started/sklearn_optcv.py
+   :language: python
+   :start-after: # [start:full_example]
+   :end-before: # [end:full_example]
+
+
+Choosing an Optimizer
+^^^^^^^^^^^^^^^^^^^^^
+
+Hyperactive provides many optimization algorithms. Here are some common choices:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 25 75
+
+   * - Optimizer
+     - Best For
+   * - ``HillClimbing``
+     - Quick local optimization, good starting point
+   * - ``RandomSearch``
+     - Exploring large search spaces, baseline comparison
+   * - ``BayesianOptimizer``
+     - Expensive evaluations, smart exploration
+   * - ``ParticleSwarmOptimizer``
+     - Multi-modal problems, avoiding local optima
+   * - ``GeneticAlgorithm``
+     - Complex landscapes, combinatorial problems
+
+Example with Bayesian Optimization:
+
+.. literalinclude:: _snippets/getting_started/bayesian_optimizer.py
+   :language: python
+   :start-after: # [start:full_example]
+   :end-before: # [end:full_example]
+
+.. literalinclude:: _snippets/getting_started/bayesian_optimizer.py
+   :language: python
+   :start-after: # [start:optimizer_usage]
+   :end-before: # [end:optimizer_usage]
+
+
+Next Steps
+----------
+
+Now that you've seen the basics, explore these topics:
+
+- :ref:`installation` - Detailed installation instructions
+- :ref:`user_guide` - In-depth tutorials and concepts
+- :ref:`api_reference` - Complete API documentation
+- :ref:`examples` - More code examples
+
+Key Concepts to Learn
+^^^^^^^^^^^^^^^^^^^^^
+
+1. **Experiments**: Abstractions that define *what* to optimize (see :ref:`user_guide_experiments`)
+2. **Optimizers**: Algorithms that define *how* to optimize (see :ref:`user_guide_optimizers`)
+3. **Search Spaces**: Define the parameter ranges to explore
+4. **Integrations**: Built-in support for sklearn, sktime, and PyTorch (see :ref:`user_guide_integrations`)
diff --git a/docs/source/index.rst b/docs/source/index.rst
new file mode 100644
index 00000000..23759cb3
--- /dev/null
+++ b/docs/source/index.rst
@@ -0,0 +1,498 @@
+.. _home:
+
+.. raw:: html
+
+   <div class="hero-section">
+      <div class="hero-content">
+         <h1 class="hero-title">Hyperactive</h1>
+         <p class="hero-tagline">A unified interface for optimization algorithms and experiments</p>
+      </div>
+   </div>
+
+   <!-- Stats are auto-counted in conf.py: n_algorithms, n_backends, n_integrations -->
+   <div class="stats-strip">
+      <div class="stat-item">
+         <div class="stat-value" id="stat-algorithms">31</div>
+         <div class="stat-label">Algorithms</div>
+      </div>
+      <div class="stat-item">
+         <div class="stat-value" id="stat-backends">3</div>
+         <div class="stat-label">Backends</div>
+      </div>
+      <div class="stat-item">
+         <div class="stat-value" id="stat-integrations">5</div>
+         <div class="stat-label">Integrations</div>
+      </div>
+      <div class="stat-item">
+         <div class="stat-value">1</div>
+         <div class="stat-label">Unified API</div>
+      </div>
+   </div>
+
+
+Hyperactive provides a collection of optimization algorithms, accessible through a unified
+experiment-based interface that separates optimization problems from algorithms. The library
+provides native implementations of algorithms from the Gradient-Free-Optimizers package
+alongside direct interfaces to Optuna and scikit-learn optimizers.
+
+.. raw:: html
+
+   <div class="badge-banner">
+      <div class="badge-items">
+         <a href="https://pypi.org/project/hyperactive/" target="_blank">
+            <img src="_static/images/badges/version.svg" alt="PyPI Version" />
+         </a>
+         <a href="https://pypi.org/project/hyperactive/" target="_blank">
+            <img src="_static/images/badges/python.svg" alt="Python Versions" />
+         </a>
+         <a href="https://github.com/SimonBlanke/Hyperactive/blob/master/LICENSE" target="_blank">
+            <img src="_static/images/badges/license.svg" alt="License" />
+         </a>
+         <a href="https://gc-os-ai.github.io/" target="_blank">
+            <img src="_static/images/badges/sponsor.svg" alt="GC.OS Sponsored" />
+         </a>
+      </div>
+   </div>
+
+
+----
+
+.. _features:
+
+Features
+================
+
+What makes Hyperactive stand out for optimization tasks.
+
+.. grid:: 1 2 3 3
+   :gutter: 4
+
+   .. grid-item-card::
+      :class-card: feature-card
+
+      **20+ Optimization Algorithms**
+      ^^^
+      From Hill Climbing to Bayesian Optimization,
+      Particle Swarm, Genetic Algorithms, and more.
+
+      +++
+      :doc:`Local, global, population-based, and sequential methods <api_reference/optimizers>`
+
+   .. grid-item-card::
+      :class-card: feature-card
+
+      **Direct ML Integration**
+      ^^^
+      First-class support for scikit-learn, sktime, skpro, and PyTorch.
+      Tune models with minimal code changes.
+
+      +++
+      :doc:`Works with any estimator implementing fit/score <user_guide/integrations>`
+
+   .. grid-item-card::
+      :class-card: feature-card
+
+      **Experiment Abstraction**
+      ^^^
+      Clean separation between *what* to optimize (experiments) and
+      *how* to optimize (algorithms).
+
+      +++
+      :doc:`Swap algorithms without changing experiment code <user_guide/experiments>`
+
+   .. grid-item-card::
+      :class-card: feature-card
+
+      **3 Optimizer Backends**
+      ^^^
+      Native GFO algorithms, Optuna samplers, and scikit-learn
+      search methods through one unified API.
+
+      +++
+      :doc:`GFO · Optuna · sklearn <user_guide/optimizers>`
+
+   .. grid-item-card::
+      :class-card: feature-card
+
+      **Mixed Parameter Spaces**
+      ^^^
+      Categorical, integer, and continuous parameters.
+      Define search spaces with NumPy arrays or lists.
+
+      +++
+      :doc:`Optuna backend supports native continuous ranges <user_guide/search_spaces>`
+
+   .. grid-item-card::
+      :class-card: feature-card
+
+      **Stable Since 2019**
+      ^^^
+      5+ years of development, comprehensive test coverage,
+      and active maintenance.
+
+      +++
+      :doc:`Type-annotated · Documented · Tested <about/history>`
+
+----
+
+Optimization Backends
+==================
+
+Hyperactive provides a unified interface to three powerful optimization backends.
+Choose the one that best fits your needs, or switch between them effortlessly.
+
+.. grid:: 1 1 3 3
+   :gutter: 4
+
+   .. grid-item-card::
+      :class-card: backend-card backend-card-gfo
+
+      `Gradient-Free-Optimizers <https://github.com/SimonBlanke/Gradient-Free-Optimizers>`__
+      ^^^
+      The native backend with 20 optimization algorithms implemented from scratch.
+      Ideal for custom objective functions and research applications.
+
+      - Hill Climbing variants
+      - Simulated Annealing
+      - Particle Swarm & Genetic Algorithms
+      - Bayesian Optimization
+      - And 15+ more algorithms
+
+      +++
+      :doc:`Explore GFO algorithms <examples/general>`
+
+   .. grid-item-card::
+      :class-card: backend-card backend-card-optuna
+
+      `Optuna <https://github.com/optuna/optuna>`__
+      ^^^
+      Industry-standard hyperparameter optimization framework with
+      state-of-the-art samplers and pruning strategies.
+
+      - Tree-Parzen Estimator (TPE)
+      - CMA-ES for continuous spaces
+      - Gaussian Process optimization
+      - Multi-objective (NSGA-II/III)
+      - Native continuous parameter support
+
+      +++
+      :doc:`Explore Optuna samplers <examples/optuna_backend>`
+
+   .. grid-item-card::
+      :class-card: backend-card backend-card-sklearn
+
+      `scikit-learn <https://github.com/scikit-learn/scikit-learn>`__
+      ^^^
+      Familiar scikit-learn search interfaces with enhanced integration
+      for cross-validation experiments.
+
+      - GridSearchCV
+      - RandomizedSearchCV
+      - HalvingGridSearchCV
+      - HalvingRandomSearchCV
+      - Drop-in sklearn compatibility
+
+      +++
+      :doc:`Explore sklearn integration <examples/sklearn_backend>`
+
+----
+
+Integrations
+============
+
+Hyperactive works seamlessly with popular machine learning frameworks.
+
+.. raw:: html
+
+   <div class="integration-grid">
+      <div class="integration-card sklearn">
+         <div class="integration-left">
+            <a href="https://github.com/scikit-learn/scikit-learn" target="_blank">scikit-learn</a>
+         </div>
+         <div class="integration-right">
+            <a href="user_guide/integrations.html#scikit-learn-integration">
+               <span class="integration-desc">Tune estimators with OptCV cross-validation</span>
+            </a>
+         </div>
+      </div>
+      <div class="integration-card sktime">
+         <div class="integration-left">
+            <a href="https://github.com/sktime/sktime" target="_blank">sktime</a>
+         </div>
+         <div class="integration-right">
+            <a href="user_guide/integrations.html#time-series-with-sktime">
+               <span class="integration-desc">Optimize forecasters and transformers</span>
+            </a>
+         </div>
+      </div>
+      <div class="integration-card skpro">
+         <div class="integration-left">
+            <a href="https://github.com/sktime/skpro" target="_blank">skpro</a>
+         </div>
+         <div class="integration-right">
+            <a href="user_guide/integrations.html#probabilistic-models-with-skpro">
+               <span class="integration-desc">Tune probabilistic regressors</span>
+            </a>
+         </div>
+      </div>
+      <div class="integration-card pytorch">
+         <div class="integration-left">
+            <a href="https://github.com/pytorch/pytorch" target="_blank">PyTorch</a>
+         </div>
+         <div class="integration-right">
+            <a href="user_guide/integrations.html#deep-learning-with-pytorch">
+               <span class="integration-desc">Optimize architectures and training parameters</span>
+            </a>
+         </div>
+      </div>
+   </div>
+
+----
+
+Quick Install
+=============
+
+.. raw:: html
+
+   <div class="segmented-tabs" id="install-tabs">
+      <nav class="segmented-tabs-nav" role="tablist">
+         <button class="segmented-tab-btn active" role="tab" aria-selected="true" aria-controls="install-panel-pip">
+            pip install
+         </button>
+         <button class="segmented-tab-btn" role="tab" aria-selected="false" aria-controls="install-panel-extras">
+            All Extras
+         </button>
+         <button class="segmented-tab-btn" role="tab" aria-selected="false" aria-controls="install-panel-sklearn">
+            sklearn
+         </button>
+         <button class="segmented-tab-btn" role="tab" aria-selected="false" aria-controls="install-panel-sktime">
+            sktime
+         </button>
+      </nav>
+      <div class="segmented-tabs-content">
+         <div class="segmented-tab-panel active" id="install-panel-pip" role="tabpanel">
+            <div class="highlight"><pre><span class="gp">$ </span>pip install hyperactive</pre></div>
+         </div>
+         <div class="segmented-tab-panel" id="install-panel-extras" role="tabpanel">
+            <div class="highlight"><pre><span class="gp">$ </span>pip install hyperactive[all_extras]</pre></div>
+         </div>
+         <div class="segmented-tab-panel" id="install-panel-sklearn" role="tabpanel">
+            <div class="highlight"><pre><span class="gp">$ </span>pip install hyperactive[sklearn-integration]</pre></div>
+         </div>
+         <div class="segmented-tab-panel" id="install-panel-sktime" role="tabpanel">
+            <div class="highlight"><pre><span class="gp">$ </span>pip install hyperactive[sktime-integration]</pre></div>
+         </div>
+      </div>
+   </div>
+
+----
+
+Quick Example
+=============
+
+Get started in just a few lines of code:
+
+.. raw:: html
+
+   <div class="vertical-tabs" id="example-tabs">
+      <nav class="vertical-tabs-nav" role="tablist">
+         <button class="vertical-tab-btn active" role="tab" aria-selected="true" aria-controls="example-panel-custom" data-tab="example-custom">
+            <span class="tab-indicator"></span>
+            <span>Custom Function</span>
+         </button>
+         <button class="vertical-tab-btn" role="tab" aria-selected="false" aria-controls="example-panel-sklearn" data-tab="example-sklearn">
+            <span class="tab-indicator"></span>
+            <span>Scikit-learn Tuning</span>
+         </button>
+         <button class="vertical-tab-btn" role="tab" aria-selected="false" aria-controls="example-panel-bayesian" data-tab="example-bayesian">
+            <span class="tab-indicator"></span>
+            <span>Bayesian Optimization</span>
+         </button>
+      </nav>
+      <div class="vertical-tabs-content">
+         <div class="vertical-tab-panel active" id="example-panel-custom" role="tabpanel">
+
+.. literalinclude:: _snippets/getting_started/index_custom_function.py
+   :language: python
+   :start-after: # [start:full_example]
+   :end-before: # [end:full_example]
+
+.. raw:: html
+
+         </div>
+         <div class="vertical-tab-panel" id="example-panel-sklearn" role="tabpanel">
+
+.. literalinclude:: _snippets/getting_started/index_sklearn_tuning.py
+   :language: python
+   :start-after: # [start:full_example]
+   :end-before: # [end:full_example]
+
+.. raw:: html
+
+         </div>
+         <div class="vertical-tab-panel" id="example-panel-bayesian" role="tabpanel">
+
+.. literalinclude:: _snippets/getting_started/index_bayesian.py
+   :language: python
+   :start-after: # [start:full_example]
+   :end-before: # [end:full_example]
+
+.. raw:: html
+
+         </div>
+      </div>
+   </div>
+
+   <script>
+   document.addEventListener('DOMContentLoaded', function() {
+      // Handle vertical tabs (Quick Example)
+      document.querySelectorAll('.vertical-tabs').forEach(function(tabContainer) {
+         const buttons = tabContainer.querySelectorAll('.vertical-tab-btn');
+         const panels = tabContainer.querySelectorAll('.vertical-tab-panel');
+
+         buttons.forEach(function(button) {
+            button.addEventListener('click', function() {
+               buttons.forEach(btn => {
+                  btn.classList.remove('active');
+                  btn.setAttribute('aria-selected', 'false');
+               });
+               panels.forEach(panel => panel.classList.remove('active'));
+
+               this.classList.add('active');
+               this.setAttribute('aria-selected', 'true');
+
+               const panelId = this.getAttribute('aria-controls');
+               const panel = document.getElementById(panelId);
+               if (panel) {
+                  panel.classList.add('active');
+               }
+            });
+         });
+      });
+
+      // Handle segmented tabs (Quick Install)
+      document.querySelectorAll('.segmented-tabs').forEach(function(tabContainer) {
+         const buttons = tabContainer.querySelectorAll('.segmented-tab-btn');
+         const panels = tabContainer.querySelectorAll('.segmented-tab-panel');
+
+         buttons.forEach(function(button) {
+            button.addEventListener('click', function() {
+               buttons.forEach(btn => {
+                  btn.classList.remove('active');
+                  btn.setAttribute('aria-selected', 'false');
+               });
+               panels.forEach(panel => panel.classList.remove('active'));
+
+               this.classList.add('active');
+               this.setAttribute('aria-selected', 'true');
+
+               const panelId = this.getAttribute('aria-controls');
+               const panel = document.getElementById(panelId);
+               if (panel) {
+                  panel.classList.add('active');
+               }
+            });
+         });
+      });
+
+      // Back to Top - inject into sidebar
+      const sidebar = document.querySelector('.bd-toc-nav.page-toc');
+      if (sidebar) {
+         const backToTopDiv = document.createElement('div');
+         backToTopDiv.className = 'back-to-top-sidebar';
+         backToTopDiv.innerHTML = '<a href="#">↑ Back to top</a>';
+         sidebar.appendChild(backToTopDiv);
+
+         backToTopDiv.querySelector('a').addEventListener('click', function(e) {
+            e.preventDefault();
+            window.scrollTo({ top: 0, behavior: 'smooth' });
+         });
+
+         window.addEventListener('scroll', function() {
+            if (window.scrollY > 400) {
+               backToTopDiv.classList.add('visible');
+            } else {
+               backToTopDiv.classList.remove('visible');
+            }
+         });
+      }
+   });
+   </script>
+
+----
+
+Contents
+========
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   get_started
+   installation
+   user_guide
+   api_reference
+   examples
+   faq
+   troubleshooting
+   get_involved
+   about
+
+.. raw:: html
+
+   <div class="contents-grid">
+      <a href="get_started.html" class="contents-card">
+         <div class="contents-card-inner">
+            <div class="contents-card-text">
+               <div class="contents-card-title">Get Started</div>
+               <div class="contents-card-desc">Quick introduction to Hyperactive</div>
+            </div>
+            <div class="contents-card-arrow">→</div>
+         </div>
+      </a>
+      <a href="installation.html" class="contents-card">
+         <div class="contents-card-inner">
+            <div class="contents-card-text">
+               <div class="contents-card-title">Installation</div>
+               <div class="contents-card-desc">Setup guide and requirements</div>
+            </div>
+            <div class="contents-card-arrow">→</div>
+         </div>
+      </a>
+      <a href="user_guide.html" class="contents-card">
+         <div class="contents-card-inner">
+            <div class="contents-card-text">
+               <div class="contents-card-title">User Guide</div>
+               <div class="contents-card-desc">In-depth tutorials and explanations</div>
+            </div>
+            <div class="contents-card-arrow">→</div>
+         </div>
+      </a>
+      <a href="api_reference.html" class="contents-card">
+         <div class="contents-card-inner">
+            <div class="contents-card-text">
+               <div class="contents-card-title">API Reference</div>
+               <div class="contents-card-desc">Technical reference for all classes</div>
+            </div>
+            <div class="contents-card-arrow">→</div>
+         </div>
+      </a>
+      <a href="examples.html" class="contents-card">
+         <div class="contents-card-inner">
+            <div class="contents-card-text">
+               <div class="contents-card-title">Examples</div>
+               <div class="contents-card-desc">Code examples and use cases</div>
+            </div>
+            <div class="contents-card-arrow">→</div>
+         </div>
+      </a>
+      <a href="get_involved.html" class="contents-card">
+         <div class="contents-card-inner">
+            <div class="contents-card-text">
+               <div class="contents-card-title">Get Involved</div>
+               <div class="contents-card-desc">Contribute to Hyperactive</div>
+            </div>
+            <div class="contents-card-arrow">→</div>
+         </div>
+      </a>
+   </div>
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
new file mode 100644
index 00000000..75554816
--- /dev/null
+++ b/docs/source/installation.rst
@@ -0,0 +1,205 @@
+.. _installation:
+
+============
+Installation
+============
+
+Hyperactive can be installed via pip and supports Python |python_version_range|.
+
+Installing Hyperactive
+----------------------
+
+Basic Installation
+^^^^^^^^^^^^^^^^^^
+
+Install Hyperactive from PyPI using pip:
+
+.. code-block:: bash
+
+    pip install hyperactive
+
+This installs Hyperactive with its core dependencies, which is sufficient for most use cases
+including scikit-learn integration.
+
+
+Installation with Extras
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+For additional functionality, you can install optional extras:
+
+.. code-block:: bash
+
+    # Full installation with all extras (Optuna, PyTorch Lightning, etc.)
+    pip install hyperactive[all_extras]
+
+    # Scikit-learn integration (included by default)
+    pip install hyperactive[sklearn-integration]
+
+    # Sktime/skpro integration for time series
+    pip install hyperactive[sktime-integration]
+
+
+Development Installation
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+To install Hyperactive for development (from source):
+
+.. code-block:: bash
+
+    # Clone the repository
+    git clone https://github.com/SimonBlanke/Hyperactive.git
+    cd Hyperactive
+
+    # Install in development mode with test dependencies
+    pip install -e ".[test]"
+
+    # Or install with all development dependencies
+    pip install -e ".[test,docs]"
+
+
+Dependencies
+------------
+
+Core Dependencies
+^^^^^^^^^^^^^^^^^
+
+Hyperactive requires the following packages (automatically installed):
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Package
+     - Purpose
+   * - ``numpy >= 1.18.1``
+     - Numerical operations and array handling
+   * - ``pandas < 3.0.0``
+     - Data manipulation and results handling
+   * - ``tqdm >= 4.48.0``
+     - Progress bars during optimization
+   * - ``gradient-free-optimizers >= 1.2.4``
+     - Core optimization algorithms
+   * - ``scikit-base < 1.0.0``
+     - Base classes for sklearn-like interfaces
+   * - ``scikit-learn < 1.8.0``
+     - Machine learning integration
+
+
+Optional Dependencies
+---------------------
+
+Depending on your use case, you may want to install additional packages:
+
+Optuna Backend
+^^^^^^^^^^^^^^
+
+For Optuna-based optimizers (TPE, CMA-ES, NSGA-II, etc.):
+
+.. code-block:: bash
+
+    pip install optuna
+
+Or include it via the ``all_extras`` option:
+
+.. code-block:: bash
+
+    pip install hyperactive[all_extras]
+
+
+Time Series (sktime/skpro)
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+For time series forecasting and probabilistic prediction:
+
+.. code-block:: bash
+
+    pip install hyperactive[sktime-integration]
+
+This installs ``sktime`` and ``skpro`` for time series optimization experiments.
+
+.. note::
+
+   Sktime integration requires Python < 3.14 due to sktime's current compatibility.
+
+
+PyTorch Lightning
+^^^^^^^^^^^^^^^^^
+
+For deep learning hyperparameter optimization:
+
+.. code-block:: bash
+
+    pip install hyperactive[all_extras]
+    # or
+    pip install lightning
+
+.. note::
+
+   PyTorch/Lightning requires Python < 3.14 for full compatibility.
+
+
+Verifying Installation
+----------------------
+
+After installation, verify that Hyperactive is working correctly:
+
+.. literalinclude:: _snippets/installation/verify_installation.py
+   :language: python
+   :start-after: # [start:verify_installation]
+   :end-before: # [end:verify_installation]
+
+
+Python Version Support
+----------------------
+
+Hyperactive officially supports Python |python_versions_list|.
+
+.. note::
+
+   The supported Python versions are automatically extracted from the project's
+   ``pyproject.toml`` classifiers.
+
+   Some optional integrations (sktime, PyTorch) may have more restrictive
+   Python version requirements. Check the specific package documentation
+   for details.
+
+
+Troubleshooting
+---------------
+
+Common Installation Issues
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+**ImportError: No module named 'gradient_free_optimizers'**
+
+This usually means the installation was incomplete. Try:
+
+.. code-block:: bash
+
+    pip install --upgrade hyperactive
+
+**MemoryError during optimization**
+
+Sequential model-based optimizers (Bayesian, TPE) can use significant memory
+for large search spaces. Reduce your search space size or use a simpler optimizer
+like ``RandomSearch`` or ``HillClimbing``.
+
+**Pickle errors with multiprocessing**
+
+Ensure all objects in your search space are serializable (no lambdas, closures,
+or bound methods). Use top-level functions and basic Python types.
+
+For more help, see the `GitHub Issues <https://github.com/SimonBlanke/Hyperactive/issues>`_.
+
+
+Using Older Versions
+--------------------
+
+If you need to use Hyperactive v4, you can install a specific version:
+
+.. code-block:: bash
+
+    pip install hyperactive==4.8.0
+
+Documentation for v4 is available at:
+`Legacy Documentation (v4) <https://simonblanke.github.io/hyperactive-documentation/5.0/>`_
diff --git a/docs/source/troubleshooting.rst b/docs/source/troubleshooting.rst
new file mode 100644
index 00000000..e265a0af
--- /dev/null
+++ b/docs/source/troubleshooting.rst
@@ -0,0 +1,39 @@
+.. _troubleshooting_guide:
+
+===============
+Troubleshooting
+===============
+
+This guide helps you diagnose and fix common issues with Hyperactive.
+
+.. toctree::
+   :maxdepth: 1
+
+   troubleshooting/installation
+   troubleshooting/runtime_errors
+   troubleshooting/performance
+   troubleshooting/results
+   troubleshooting/experiments
+   troubleshooting/getting_help
+
+
+Overview
+--------
+
+:ref:`troubleshooting_installation`
+    Import errors, missing modules, and dependency issues.
+
+:ref:`troubleshooting_runtime`
+    AttributeError, TypeError, and ValueError during execution.
+
+:ref:`troubleshooting_performance`
+    Slow optimization and memory errors.
+
+:ref:`troubleshooting_results`
+    Inconsistent results, local optima, and unexpected scores.
+
+:ref:`troubleshooting_experiments`
+    Issues with sklearn, PyTorch Lightning, and other integrations.
+
+:ref:`troubleshooting_help`
+    Where to get additional support.
diff --git a/docs/source/troubleshooting/experiments.rst b/docs/source/troubleshooting/experiments.rst
new file mode 100644
index 00000000..0dd60150
--- /dev/null
+++ b/docs/source/troubleshooting/experiments.rst
@@ -0,0 +1,53 @@
+.. _troubleshooting_experiments:
+
+===========================
+Experiment-Specific Issues
+===========================
+
+SklearnCvExperiment Not Finding Best Parameters
+------------------------------------------------
+
+**Cause**: Search space doesn't include good values or not enough iterations.
+
+**Solutions**:
+
+1. Verify search space includes reasonable values:
+
+   .. code-block:: python
+
+       # Make sure these are sensible for your model
+       search_space = {
+           "n_estimators": [10, 50, 100, 200, 500],
+           "max_depth": [None, 3, 5, 10, 20],
+       }
+
+2. Increase iterations or use smarter optimizer:
+
+   .. code-block:: python
+
+       optimizer = BayesianOptimizer(
+           search_space=space,
+           n_iter=200,  # More iterations
+           experiment=experiment,
+       )
+
+
+PyTorch Lightning Metric Not Found
+----------------------------------
+
+**Cause**: The metric name doesn't match what's logged during training.
+
+**Solution**: Check your Lightning module logs the correct metric:
+
+.. code-block:: python
+
+    class MyModel(L.LightningModule):
+        def validation_step(self, batch, batch_idx):
+            loss = self.compute_loss(batch)
+            self.log("val_loss", loss)  # Must match objective_metric
+
+    experiment = TorchLightningExperiment(
+        lightning_module=MyModel,
+        objective_metric="val_loss",  # Must match self.log name
+        ...
+    )
diff --git a/docs/source/troubleshooting/getting_help.rst b/docs/source/troubleshooting/getting_help.rst
new file mode 100644
index 00000000..c594961a
--- /dev/null
+++ b/docs/source/troubleshooting/getting_help.rst
@@ -0,0 +1,20 @@
+.. _troubleshooting_help:
+
+================
+Getting More Help
+================
+
+If your issue isn't covered in this troubleshooting guide:
+
+1. **Check the FAQ** — See :ref:`faq` for common questions
+
+2. **Search existing issues** — Browse `GitHub Issues <https://github.com/SimonBlanke/Hyperactive/issues>`_
+
+3. **Open a new issue** — Include:
+
+   - Your code (minimal reproducible example)
+   - Full error traceback
+   - Hyperactive version (``hyperactive.__version__``)
+   - Python version
+
+4. **Join the community** — Ask on `Discord <https://discord.gg/7uKdHfdcJG>`_
diff --git a/docs/source/troubleshooting/installation.rst b/docs/source/troubleshooting/installation.rst
new file mode 100644
index 00000000..ced1c9d8
--- /dev/null
+++ b/docs/source/troubleshooting/installation.rst
@@ -0,0 +1,61 @@
+.. _troubleshooting_installation:
+
+===================
+Installation Issues
+===================
+
+ImportError: No module named 'hyperactive'
+------------------------------------------
+
+**Cause**: Hyperactive is not installed or installed in a different environment.
+
+**Solution**:
+
+.. code-block:: bash
+
+    pip install hyperactive
+
+    # Or with extras
+    pip install hyperactive[all_extras]
+
+Verify installation:
+
+.. code-block:: bash
+
+    python -c "import hyperactive; print(hyperactive.__version__)"
+
+
+ImportError: cannot import name 'Hyperactive'
+---------------------------------------------
+
+**Cause**: You're using v4 code with Hyperactive v5. The ``Hyperactive`` class
+was removed in v5.
+
+**Solution**: Update your imports. See :ref:`user_guide_migration` for details.
+
+.. code-block:: python
+
+    # Old (v4)
+    from hyperactive import Hyperactive
+
+    # New (v5)
+    from hyperactive.opt.gfo import HillClimbing
+
+
+Missing Optional Dependencies
+-----------------------------
+
+**Cause**: Some features require additional packages.
+
+**Solution**: Install the appropriate extras:
+
+.. code-block:: bash
+
+    # For scikit-learn integration
+    pip install hyperactive[sklearn-integration]
+
+    # For Optuna backend
+    pip install hyperactive[optuna]
+
+    # For all extras
+    pip install hyperactive[all_extras]
diff --git a/docs/source/troubleshooting/performance.rst b/docs/source/troubleshooting/performance.rst
new file mode 100644
index 00000000..84298d0a
--- /dev/null
+++ b/docs/source/troubleshooting/performance.rst
@@ -0,0 +1,69 @@
+.. _troubleshooting_performance:
+
+==================
+Performance Issues
+==================
+
+Optimization is Very Slow
+-------------------------
+
+**Possible causes and solutions**:
+
+1. **Slow objective function**
+
+   The optimizer can only be as fast as your objective. Consider:
+
+   - Reducing cross-validation folds (``cv=3`` instead of ``cv=10``)
+   - Using a subset of data during tuning
+   - Using a simpler model for initial exploration
+
+2. **Too many iterations**
+
+   Start with fewer iterations:
+
+   .. code-block:: python
+
+       optimizer = HillClimbing(
+           search_space=space,
+           n_iter=50,  # Start small
+           experiment=objective,
+       )
+
+3. **Overly large search space**
+
+   Reduce granularity or the number of parameters:
+
+   .. code-block:: python
+
+       # Instead of 1000 values
+       "learning_rate": np.linspace(0.001, 0.1, 1000)
+
+       # Use 20-50 values
+       "learning_rate": np.logspace(-3, -1, 20)
+
+
+Memory Errors
+-------------
+
+**Cause**: Very large search spaces can cause memory issues with some optimizers,
+especially those that cache all combinations.
+
+**Solution**:
+
+1. Reduce search space size
+2. Use sampling-based optimizers (``RandomSearch``, ``BayesianOptimizer``)
+3. Use coarser parameter granularity
+
+.. code-block:: python
+
+    # High memory usage
+    search_space = {
+        "a": np.linspace(0, 1, 10000),
+        "b": np.linspace(0, 1, 10000),
+    }  # 100 million combinations!
+
+    # Lower memory usage
+    search_space = {
+        "a": np.linspace(0, 1, 100),
+        "b": np.linspace(0, 1, 100),
+    }  # 10,000 combinations
diff --git a/docs/source/troubleshooting/results.rst b/docs/source/troubleshooting/results.rst
new file mode 100644
index 00000000..bb6ef9d8
--- /dev/null
+++ b/docs/source/troubleshooting/results.rst
@@ -0,0 +1,78 @@
+.. _troubleshooting_results:
+
+====================
+Optimization Results
+====================
+
+Results Vary Between Runs
+-------------------------
+
+**Cause**: Optimization algorithms are stochastic.
+
+**Solution**: Set a random seed for reproducibility:
+
+.. code-block:: python
+
+    optimizer = HillClimbing(
+        search_space=space,
+        n_iter=100,
+        experiment=objective,
+        random_state=42,
+    )
+
+
+Optimizer Gets Stuck in Local Optima
+------------------------------------
+
+**Cause**: Local search algorithms (like HillClimbing) can get trapped.
+
+**Solutions**:
+
+1. Use a global search algorithm:
+
+   .. code-block:: python
+
+       from hyperactive.opt.gfo import RandomSearch, BayesianOptimizer
+
+2. Use population-based methods:
+
+   .. code-block:: python
+
+       from hyperactive.opt.gfo import ParticleSwarmOptimizer, GeneticAlgorithm
+
+3. Increase exploration in local search:
+
+   .. code-block:: python
+
+       optimizer = HillClimbing(
+           search_space=space,
+           n_iter=100,
+           experiment=objective,
+           epsilon=0.2,  # Larger steps
+       )
+
+
+Best Score is Very Low or Negative
+----------------------------------
+
+**Check these**:
+
+1. **Objective function errors** — Make sure your objective doesn't crash:
+
+   .. code-block:: python
+
+       def objective(params):
+           try:
+               score = evaluate(params)
+               return score
+           except Exception as e:
+               print(f"Error: {e}")  # Debug
+               return -np.inf
+
+2. **Sign convention** — Hyperactive maximizes. Negate if minimizing:
+
+   .. code-block:: python
+
+       def objective(params):
+           error = compute_error(params)
+           return -error  # Negate for minimization
diff --git a/docs/source/troubleshooting/runtime_errors.rst b/docs/source/troubleshooting/runtime_errors.rst
new file mode 100644
index 00000000..a0b29a05
--- /dev/null
+++ b/docs/source/troubleshooting/runtime_errors.rst
@@ -0,0 +1,60 @@
+.. _troubleshooting_runtime:
+
+==============
+Runtime Errors
+==============
+
+AttributeError: 'X' object has no attribute 'run'
+-------------------------------------------------
+
+**Cause**: Using v4 method names with v5 optimizers.
+
+**Solution**: Use ``.solve()`` instead of ``.run()``:
+
+.. code-block:: python
+
+    # Old (v4)
+    hyper.run()
+
+    # New (v5)
+    best_params = optimizer.solve()
+
+
+TypeError: unexpected keyword argument
+--------------------------------------
+
+**Cause**: Parameter passing changed in v5. All configuration now goes
+to the optimizer constructor.
+
+**Solution**:
+
+.. code-block:: python
+
+    # Old (v4)
+    hyper.add_search(model, space, optimizer=opt, n_iter=100)
+
+    # New (v5)
+    optimizer = HillClimbing(
+        search_space=space,
+        n_iter=100,
+        experiment=objective,
+    )
+
+
+ValueError: Parameters do not match
+-----------------------------------
+
+**Cause**: Your search space keys don't match what the experiment expects.
+
+**Solution**: Ensure search space keys match the parameters your objective
+function or experiment expects:
+
+.. code-block:: python
+
+    # Search space defines "learning_rate"
+    search_space = {"learning_rate": [0.01, 0.1]}
+
+    # Objective must use the same key
+    def objective(params):
+        lr = params["learning_rate"]  # Not "lr" or "LearningRate"
+        ...
diff --git a/docs/source/user_guide.rst b/docs/source/user_guide.rst
new file mode 100644
index 00000000..6d6f7e23
--- /dev/null
+++ b/docs/source/user_guide.rst
@@ -0,0 +1,99 @@
+.. _user_guide:
+
+==========
+User Guide
+==========
+
+This guide covers Hyperactive's core concepts and features in depth.
+Whether you're new to hyperparameter optimization or an experienced practitioner,
+you'll find detailed explanations and practical examples here.
+
+.. note::
+
+   Some code snippets in this guide are **illustrative** and may contain
+   placeholders (like ``score`` or ``SomeOptimizer``). For complete, runnable
+   examples, see the :ref:`examples` or :ref:`get_started` sections.
+
+.. toctree::
+   :maxdepth: 1
+
+   user_guide/introduction
+   user_guide/search_spaces
+   user_guide/optimizers
+   user_guide/experiments
+   user_guide/integrations
+   user_guide/migration
+
+
+Overview
+--------
+
+Hyperactive v5 introduces a clean **experiment-based architecture** that separates
+optimization algorithms from optimization problems:
+
+- **Experiments** define *what* to optimize — the objective function and evaluation logic
+- **Optimizers** define *how* to optimize — the search strategy and algorithm
+
+This design allows you to:
+
+- Mix and match any optimizer with any experiment type
+- Create reusable experiment definitions for common ML tasks
+- Easily switch between different optimization strategies
+- Build complex optimization workflows with consistent interfaces
+
+Basic Workflow
+^^^^^^^^^^^^^^
+
+Every Hyperactive optimization follows this pattern:
+
+.. code-block:: python
+
+    from hyperactive.opt.gfo import HillClimbing
+
+    # 1. Define the experiment (what to optimize)
+    def objective(params):
+        return score  # Hyperactive maximizes this
+
+    # 2. Define the search space
+    search_space = {
+        "param1": [value1, value2, ...],
+        "param2": [value1, value2, ...],
+    }
+
+    # 3. Choose an optimizer (how to optimize)
+    optimizer = HillClimbing(
+        search_space=search_space,
+        n_iter=100,
+        experiment=objective,
+    )
+
+    # 4. Run the optimization
+    best_params = optimizer.solve()
+
+
+Guide Contents
+--------------
+
+:ref:`user_guide_introduction`
+    Core concepts: optimizers, experiments, and search spaces.
+    Start here to understand Hyperactive's architecture.
+
+:ref:`user_guide_search_spaces`
+    Best practices for designing search spaces.
+    Covers scaling, granularity, and common patterns.
+
+:ref:`user_guide_optimizers`
+    Detailed guide to choosing and configuring optimizers.
+    Covers local search, global search, population methods, and Bayesian approaches.
+
+:ref:`user_guide_experiments`
+    How to define optimization problems using experiments.
+    Includes custom functions and built-in ML experiments.
+
+:ref:`user_guide_integrations`
+    Framework integrations for scikit-learn, sktime, skpro, and PyTorch.
+    Drop-in replacements for GridSearchCV and similar tools.
+
+:ref:`user_guide_migration`
+    Migration guide for upgrading from Hyperactive v4 to v5.
+    Covers API changes, new patterns, and troubleshooting.
diff --git a/docs/source/user_guide/experiments.rst b/docs/source/user_guide/experiments.rst
new file mode 100644
index 00000000..7ee388ef
--- /dev/null
+++ b/docs/source/user_guide/experiments.rst
@@ -0,0 +1,137 @@
+.. _user_guide_experiments:
+
+===========
+Experiments
+===========
+
+Experiments define *what* to optimize in Hyperactive. They encapsulate the objective
+function and any evaluation logic needed to score a set of parameters.
+
+Defining Experiments
+--------------------
+
+There are two ways to define experiments in Hyperactive:
+
+1. **Custom functions** — Simple callables for any optimization problem
+2. **Built-in experiment classes** — Pre-built experiments for common ML tasks
+
+
+Custom Objective Functions
+--------------------------
+
+The simplest way to define an experiment is as a Python function that takes
+a parameter dictionary and returns a score:
+
+.. literalinclude:: ../_snippets/user_guide/experiments.py
+   :language: python
+   :start-after: # [start:simple_objective]
+   :end-before: # [end:simple_objective]
+
+Key points:
+
+- The function receives a dictionary with parameter names as keys
+- It must return a single numeric value (the score)
+- Hyperactive **maximizes** this score by default
+- To minimize, negate your loss function (as shown above)
+
+
+Example: Optimizing a Mathematical Function
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. literalinclude:: ../_snippets/user_guide/experiments.py
+   :language: python
+   :start-after: # [start:ackley_function]
+   :end-before: # [end:ackley_function]
+
+
+Example: Optimizing with External Resources
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Your objective function can use any Python code:
+
+.. literalinclude:: ../_snippets/user_guide/experiments.py
+   :language: python
+   :start-after: # [start:external_simulation]
+   :end-before: # [end:external_simulation]
+
+
+Built-in Experiment Classes
+---------------------------
+
+For common machine learning tasks, Hyperactive provides ready-to-use experiment classes
+that handle cross-validation, scoring, and other details.
+
+
+SklearnCvExperiment
+^^^^^^^^^^^^^^^^^^^
+
+For optimizing scikit-learn estimators with cross-validation:
+
+.. literalinclude:: ../_snippets/user_guide/experiments.py
+   :language: python
+   :start-after: # [start:sklearn_cv_experiment]
+   :end-before: # [end:sklearn_cv_experiment]
+
+
+SktimeForecastingExperiment
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+For time series forecasting optimization (requires ``sktime``):
+
+.. literalinclude:: ../_snippets/user_guide/experiments.py
+   :language: python
+   :start-after: # [start:sktime_forecasting]
+   :end-before: # [end:sktime_forecasting]
+
+
+TorchExperiment
+^^^^^^^^^^^^^^^
+
+For PyTorch Lightning model optimization (requires ``lightning``):
+
+.. literalinclude:: ../_snippets/user_guide/experiments.py
+   :language: python
+   :start-after: # [start:torch_experiment]
+   :end-before: # [end:torch_experiment]
+
+
+Benchmark Experiments
+---------------------
+
+Hyperactive includes standard benchmark functions for testing optimizers:
+
+.. literalinclude:: ../_snippets/user_guide/experiments.py
+   :language: python
+   :start-after: # [start:benchmark_experiments]
+   :end-before: # [end:benchmark_experiments]
+
+
+Using the score() Method
+------------------------
+
+Experiments can also be evaluated directly using the ``score()`` method:
+
+.. literalinclude:: ../_snippets/user_guide/experiments.py
+   :language: python
+   :start-after: # [start:score_method]
+   :end-before: # [end:score_method]
+
+
+Tips for Designing Experiments
+------------------------------
+
+1. **Return meaningful scores**: Ensure your score reflects what you want to optimize.
+   Higher is better (Hyperactive maximizes).
+
+2. **Handle errors gracefully**: If a parameter combination fails, return a very
+   low score (e.g., ``-np.inf``) rather than raising an exception.
+
+3. **Consider computation time**: For expensive experiments, use efficient optimizers
+   like ``BayesianOptimizer`` that learn from previous evaluations.
+
+4. **Use reproducibility**: Set random seeds in your experiment for consistent results.
+
+.. literalinclude:: ../_snippets/user_guide/experiments.py
+   :language: python
+   :start-after: # [start:robust_objective]
+   :end-before: # [end:robust_objective]
diff --git a/docs/source/user_guide/integrations.rst b/docs/source/user_guide/integrations.rst
new file mode 100644
index 00000000..d45e5da3
--- /dev/null
+++ b/docs/source/user_guide/integrations.rst
@@ -0,0 +1,147 @@
+.. _user_guide_integrations:
+
+======================
+Framework Integrations
+======================
+
+Hyperactive provides seamless integrations with popular machine learning frameworks.
+These integrations offer drop-in replacements for tools like ``GridSearchCV``,
+making it easy to use any Hyperactive optimizer with your existing code.
+
+
+Scikit-Learn Integration
+------------------------
+
+The ``OptCV`` class provides a scikit-learn compatible interface for hyperparameter
+tuning. It works like ``GridSearchCV`` but supports any Hyperactive optimizer.
+
+Basic Usage
+^^^^^^^^^^^
+
+.. literalinclude:: ../_snippets/user_guide/integrations.py
+   :language: python
+   :start-after: # [start:optcv_basic]
+   :end-before: # [end:optcv_basic]
+
+
+Using Different Optimizers
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Any Hyperactive optimizer works with ``OptCV``:
+
+.. literalinclude:: ../_snippets/user_guide/integrations.py
+   :language: python
+   :start-after: # [start:different_optimizers]
+   :end-before: # [end:different_optimizers]
+
+
+Pipeline Integration
+^^^^^^^^^^^^^^^^^^^^
+
+``OptCV`` works with sklearn pipelines:
+
+.. literalinclude:: ../_snippets/user_guide/integrations.py
+   :language: python
+   :start-after: # [start:pipeline_integration]
+   :end-before: # [end:pipeline_integration]
+
+
+Time Series with Sktime
+-----------------------
+
+Hyperactive integrates with ``sktime`` for time series forecasting optimization.
+
+.. note::
+
+   Requires ``pip install hyperactive[sktime-integration]``
+
+
+Forecasting Optimization
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+Use ``ForecastingOptCV`` to tune forecasters:
+
+.. literalinclude:: ../_snippets/user_guide/integrations.py
+   :language: python
+   :start-after: # [start:forecasting_optcv]
+   :end-before: # [end:forecasting_optcv]
+
+
+Time Series Classification
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Use ``TSCOptCV`` for time series classification:
+
+.. literalinclude:: ../_snippets/user_guide/integrations.py
+   :language: python
+   :start-after: # [start:tsc_optcv]
+   :end-before: # [end:tsc_optcv]
+
+
+Probabilistic Prediction with Skpro
+-----------------------------------
+
+For probabilistic regression with ``skpro``:
+
+.. literalinclude:: ../_snippets/user_guide/integrations.py
+   :language: python
+   :start-after: # [start:skpro_experiment]
+   :end-before: # [end:skpro_experiment]
+
+
+PyTorch Lightning Integration
+-----------------------------
+
+For deep learning hyperparameter optimization with PyTorch Lightning:
+
+.. note::
+
+   Requires ``pip install hyperactive[all_extras]`` or ``pip install lightning``
+
+.. literalinclude:: ../_snippets/user_guide/integrations.py
+   :language: python
+   :start-after: # [start:pytorch_lightning]
+   :end-before: # [end:pytorch_lightning]
+
+
+Choosing the Right Integration
+------------------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 25 25 50
+
+   * - Framework
+     - Integration Class
+     - Use Case
+   * - scikit-learn
+     - ``OptCV``
+     - Classification, regression, pipelines
+   * - sktime
+     - ``ForecastingOptCV``
+     - Time series forecasting
+   * - sktime
+     - ``TSCOptCV``
+     - Time series classification
+   * - skpro
+     - ``SkproProbaRegExperiment``
+     - Probabilistic regression
+   * - PyTorch Lightning
+     - ``TorchExperiment``
+     - Deep learning models
+
+
+Tips for Using Integrations
+---------------------------
+
+1. **Match the interface**: Use ``OptCV`` when you want sklearn-compatible behavior
+   (fit/predict). Use experiment classes when you want more control.
+
+2. **Consider evaluation cost**: Deep learning experiments are expensive.
+   Use efficient optimizers like ``BayesianOptimizer`` with fewer iterations.
+
+3. **Use appropriate CV strategies**: Match your cross-validation to your problem
+   (e.g., ``TimeSeriesSplit`` for time series, stratified splits for imbalanced data).
+
+4. **Start simple**: Begin with ``GridSearch`` or ``RandomSearch`` to establish
+   baselines before using more sophisticated optimizers.
diff --git a/docs/source/user_guide/introduction.rst b/docs/source/user_guide/introduction.rst
new file mode 100644
index 00000000..e000942a
--- /dev/null
+++ b/docs/source/user_guide/introduction.rst
@@ -0,0 +1,179 @@
+.. _user_guide_introduction:
+
+============
+Introduction
+============
+
+This page introduces Hyperactive's core concepts: optimizers, experiments, and search spaces.
+Understanding these concepts will help you use Hyperactive effectively for any optimization task.
+
+
+Core Concepts
+-------------
+
+Hyperactive is built around three key concepts:
+
+1. **Experiments** — Define *what* to optimize (the objective function)
+2. **Optimizers** — Define *how* to optimize (the search algorithm)
+3. **Search Spaces** — Define *where* to search (the parameter ranges)
+
+
+Experiments: What to Optimize
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+An **experiment** represents your optimization problem. It takes parameters as input
+and returns a score that Hyperactive will maximize.
+
+The simplest experiment is a Python function:
+
+.. literalinclude:: ../_snippets/user_guide/introduction.py
+   :language: python
+   :start-after: # [start:simple_objective]
+   :end-before: # [end:simple_objective]
+
+For machine learning, Hyperactive provides built-in experiments:
+
+.. literalinclude:: ../_snippets/user_guide/introduction.py
+   :language: python
+   :start-after: # [start:sklearn_experiment_intro]
+   :end-before: # [end:sklearn_experiment_intro]
+
+See :ref:`user_guide_experiments` for more details.
+
+
+Optimizers: How to Optimize
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+An **optimizer** is the algorithm that explores the search space to find the best parameters.
+Hyperactive provides 20+ optimizers in different categories:
+
+.. literalinclude:: ../_snippets/user_guide/introduction.py
+   :language: python
+   :start-after: # [start:optimizer_imports]
+   :end-before: # [end:optimizer_imports]
+
+Each optimizer has different characteristics:
+
+- **Local search** (HillClimbing, SimulatedAnnealing): Fast, may get stuck in local optima
+- **Global search** (RandomSearch, GridSearch): Thorough exploration, slower
+- **Population methods** (GeneticAlgorithm, ParticleSwarm): Good for complex landscapes
+- **Sequential methods** (BayesianOptimizer, TPE): Smart exploration, best for expensive evaluations
+
+See :ref:`user_guide_optimizers` for a complete guide.
+
+
+Search Spaces: Where to Search
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+A **search space** defines the possible values for each parameter.
+Use dictionaries with lists or NumPy arrays:
+
+.. literalinclude:: ../_snippets/user_guide/introduction.py
+   :language: python
+   :start-after: # [start:search_space_definition]
+   :end-before: # [end:search_space_definition]
+
+.. tip::
+
+    Keep search spaces reasonably sized. Very large spaces (>10^8 combinations)
+    can cause memory issues with some optimizers.
+
+
+Basic Workflow
+--------------
+
+Here's the complete workflow for using Hyperactive:
+
+Step 1: Define Your Experiment
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Either as a function or using built-in experiment classes:
+
+.. literalinclude:: ../_snippets/user_guide/introduction.py
+   :language: python
+   :start-after: # [start:workflow_experiment_options]
+   :end-before: # [end:workflow_experiment_options]
+
+
+Step 2: Define the Search Space
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. literalinclude:: ../_snippets/user_guide/introduction.py
+   :language: python
+   :start-after: # [start:workflow_search_space]
+   :end-before: # [end:workflow_search_space]
+
+
+Step 3: Choose an Optimizer
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. literalinclude:: ../_snippets/user_guide/introduction.py
+   :language: python
+   :start-after: # [start:workflow_optimizer]
+   :end-before: # [end:workflow_optimizer]
+
+
+Step 4: Run the Optimization
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. literalinclude:: ../_snippets/user_guide/introduction.py
+   :language: python
+   :start-after: # [start:workflow_solve]
+   :end-before: # [end:workflow_solve]
+
+
+Common Optimizer Parameters
+---------------------------
+
+Most optimizers share these parameters:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 20 15 65
+
+   * - Parameter
+     - Type
+     - Description
+   * - ``search_space``
+     - dict
+     - Maps parameter names to possible values
+   * - ``n_iter``
+     - int
+     - Number of optimization iterations
+   * - ``experiment``
+     - callable
+     - The objective function or experiment object
+   * - ``random_state``
+     - int
+     - Seed for reproducibility
+   * - ``initialize``
+     - dict
+     - Control initial population (warm starts, etc.)
+
+
+Warm Starting
+^^^^^^^^^^^^^
+
+You can provide starting points for optimization:
+
+.. literalinclude:: ../_snippets/user_guide/introduction.py
+   :language: python
+   :start-after: # [start:warm_starting]
+   :end-before: # [end:warm_starting]
+
+
+Tips for Effective Optimization
+-------------------------------
+
+1. **Start simple**: Begin with ``HillClimbing`` or ``RandomSearch`` to establish baselines.
+
+2. **Right-size your search space**: Large spaces need more iterations. Consider using
+   ``np.logspace`` for parameters that span orders of magnitude.
+
+3. **Use appropriate iterations**: More iterations = better exploration, but longer runtime.
+   A good rule of thumb: at least 10x the number of parameters.
+
+4. **Set random_state**: For reproducible results, always set a random seed.
+
+5. **Consider your budget**: For expensive evaluations (training large models),
+   use smart optimizers like ``BayesianOptimizer`` that learn from previous evaluations.
diff --git a/docs/source/user_guide/migration.rst b/docs/source/user_guide/migration.rst
new file mode 100644
index 00000000..6a73c075
--- /dev/null
+++ b/docs/source/user_guide/migration.rst
@@ -0,0 +1,361 @@
+.. _user_guide_migration:
+
+======================
+Migration Guide (v4→v5)
+======================
+
+This guide helps you migrate from Hyperactive v4 to v5. Version 5 introduces
+a new experiment-based architecture with a simplified API.
+
+.. note::
+
+   If you're still using Hyperactive v4 and need documentation for that version,
+   see the `Legacy Documentation (v4) <https://simonblanke.github.io/hyperactive-documentation/5.0/>`_.
+
+Quick Summary
+-------------
+
+The main changes in v5 are:
+
+1. **No more ``Hyperactive`` class** — Optimizers are used directly
+2. **``.run()`` replaced with ``.solve()``** — Single method for optimization
+3. **New import paths** — ``hyperactive.opt.gfo`` instead of ``hyperactive.optimizers``
+4. **Experiment abstraction** — Built-in ML experiments for scikit-learn, sktime, etc.
+5. **Constructor-based configuration** — All parameters passed to optimizer constructor
+
+
+Basic Migration
+---------------
+
+Here's how to convert a v4 script to v5:
+
+**v4 (Old)**
+
+.. code-block:: python
+
+    from hyperactive import Hyperactive
+    from hyperactive.optimizers import HillClimbingOptimizer
+
+    def model(opt):
+        # Use opt["param"] to access parameters
+        score = -(opt["x"] ** 2)
+        return score
+
+    search_space = {
+        "x": list(range(-10, 10)),
+    }
+
+    # Create optimizer separately
+    optimizer = HillClimbingOptimizer(epsilon=0.1)
+
+    # Create Hyperactive instance and add search
+    hyper = Hyperactive()
+    hyper.add_search(
+        model,
+        search_space,
+        optimizer=optimizer,
+        n_iter=100,
+    )
+    hyper.run()
+
+    # Access results
+    best_params = hyper.best_para(model)
+    best_score = hyper.best_score(model)
+
+**v5 (New)**
+
+.. code-block:: python
+
+    from hyperactive.opt.gfo import HillClimbing
+
+    def objective(params):
+        # Use params["param"] to access parameters
+        score = -(params["x"] ** 2)
+        return score
+
+    search_space = {
+        "x": list(range(-10, 10)),
+    }
+
+    # Configure optimizer directly
+    optimizer = HillClimbing(
+        search_space=search_space,
+        n_iter=100,
+        experiment=objective,
+        epsilon=0.1,
+    )
+
+    # Run optimization
+    best_params = optimizer.solve()
+
+    # Access results
+    best_score = optimizer.best_score_
+
+
+Import Path Changes
+-------------------
+
+The optimizer imports have changed:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 50 50
+
+   * - v4 Import
+     - v5 Import
+   * - ``from hyperactive import Hyperactive``
+     - Not needed
+   * - ``from hyperactive.optimizers import HillClimbingOptimizer``
+     - ``from hyperactive.opt.gfo import HillClimbing``
+   * - ``from hyperactive.optimizers import RandomSearchOptimizer``
+     - ``from hyperactive.opt.gfo import RandomSearch``
+   * - ``from hyperactive.optimizers import BayesianOptimizer``
+     - ``from hyperactive.opt.gfo import BayesianOptimizer``
+
+.. tip::
+
+   In v5, you can also import optimizers directly from ``hyperactive.opt``:
+
+   .. code-block:: python
+
+       from hyperactive.opt import HillClimbing, RandomSearch, BayesianOptimizer
+
+
+Optimizer Name Changes
+----------------------
+
+Some optimizer class names have changed:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 50 50
+
+   * - v4 Name
+     - v5 Name
+   * - ``HillClimbingOptimizer``
+     - ``HillClimbing``
+   * - ``RandomSearchOptimizer``
+     - ``RandomSearch``
+   * - ``ParticleSwarmOptimizer``
+     - ``ParticleSwarmOptimizer`` (unchanged)
+   * - ``BayesianOptimizer``
+     - ``BayesianOptimizer`` (unchanged)
+   * - ``TreeStructuredParzenEstimators``
+     - ``TreeStructuredParzenEstimators`` (unchanged)
+
+
+Method Changes
+--------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 30 40
+
+   * - Action
+     - v4
+     - v5
+   * - Run optimization
+     - ``hyper.run()``
+     - ``optimizer.solve()``
+   * - Get best params
+     - ``hyper.best_para(model)``
+     - ``optimizer.best_params_``
+   * - Get best score
+     - ``hyper.best_score(model)``
+     - ``optimizer.best_score_``
+
+
+Scikit-learn Integration
+------------------------
+
+v5 introduces experiment classes for cleaner ML integration.
+
+**v4 (Old)**
+
+.. code-block:: python
+
+    from sklearn.datasets import load_iris
+    from sklearn.ensemble import RandomForestClassifier
+    from sklearn.model_selection import cross_val_score
+    from hyperactive import Hyperactive
+    from hyperactive.optimizers import HillClimbingOptimizer
+
+    X, y = load_iris(return_X_y=True)
+
+    def model(opt):
+        clf = RandomForestClassifier(
+            n_estimators=opt["n_estimators"],
+            max_depth=opt["max_depth"],
+        )
+        return cross_val_score(clf, X, y, cv=3).mean()
+
+    search_space = {
+        "n_estimators": [10, 50, 100],
+        "max_depth": [3, 5, 10],
+    }
+
+    hyper = Hyperactive()
+    hyper.add_search(model, search_space, n_iter=50)
+    hyper.run()
+
+**v5 (New) — Using SklearnCvExperiment**
+
+.. code-block:: python
+
+    from sklearn.datasets import load_iris
+    from sklearn.ensemble import RandomForestClassifier
+    from hyperactive.experiment.integrations import SklearnCvExperiment
+    from hyperactive.opt.gfo import HillClimbing
+
+    X, y = load_iris(return_X_y=True)
+
+    experiment = SklearnCvExperiment(
+        estimator=RandomForestClassifier(),
+        X=X, y=y, cv=3,
+    )
+
+    search_space = {
+        "n_estimators": [10, 50, 100],
+        "max_depth": [3, 5, 10],
+    }
+
+    optimizer = HillClimbing(
+        search_space=search_space,
+        n_iter=50,
+        experiment=experiment,
+    )
+    best_params = optimizer.solve()
+
+**v5 (Alternative) — Using custom function**
+
+The v4-style custom function still works in v5:
+
+.. code-block:: python
+
+    from sklearn.datasets import load_iris
+    from sklearn.ensemble import RandomForestClassifier
+    from sklearn.model_selection import cross_val_score
+    from hyperactive.opt.gfo import HillClimbing
+
+    X, y = load_iris(return_X_y=True)
+
+    def objective(params):
+        clf = RandomForestClassifier(
+            n_estimators=params["n_estimators"],
+            max_depth=params["max_depth"],
+        )
+        return cross_val_score(clf, X, y, cv=3).mean()
+
+    search_space = {
+        "n_estimators": [10, 50, 100],
+        "max_depth": [3, 5, 10],
+    }
+
+    optimizer = HillClimbing(
+        search_space=search_space,
+        n_iter=50,
+        experiment=objective,
+    )
+    best_params = optimizer.solve()
+
+
+New Features in v5
+------------------
+
+v5 introduces several new features:
+
+**Experiment Classes**
+
+Pre-built experiments for common ML tasks:
+
+- ``SklearnCvExperiment`` — scikit-learn cross-validation
+- ``SktimeForecastingExperiment`` — sktime forecasting
+- ``SktimeClassificationExperiment`` — sktime time series classification
+- ``SkproProbaRegExperiment`` — skpro probabilistic regression
+- ``TorchExperiment`` — PyTorch Lightning models
+
+**Optuna Backend**
+
+Access Optuna optimizers through Hyperactive:
+
+.. code-block:: python
+
+    from hyperactive.opt.optuna import TPEOptimizer, CmaEsOptimizer
+
+**sklearn-Compatible Interface**
+
+Drop-in replacement for ``GridSearchCV``:
+
+.. code-block:: python
+
+    from hyperactive.integrations.sklearn import OptCV
+
+    search = OptCV(estimator=clf, param_space=space, n_iter=50)
+    search.fit(X, y)
+
+
+Removed Features
+----------------
+
+The following v4 features are no longer available in v5:
+
+- ``Hyperactive.add_search()`` — Use optimizer constructor instead
+- ``Hyperactive.run()`` — Use ``optimizer.solve()`` instead
+- ``search_data`` parameter — Data collection handled differently
+- ``memory`` parameter — Memory features restructured
+- Multiple parallel searches — Use separate optimizer instances
+
+
+Troubleshooting Migration
+-------------------------
+
+**ImportError: cannot import name 'Hyperactive'**
+
+The ``Hyperactive`` class no longer exists. Use optimizers directly:
+
+.. code-block:: python
+
+    # Old
+    from hyperactive import Hyperactive
+
+    # New
+    from hyperactive.opt.gfo import HillClimbing
+
+**AttributeError: 'HillClimbing' object has no attribute 'run'**
+
+The ``.run()`` method is now ``.solve()``:
+
+.. code-block:: python
+
+    # Old
+    hyper.run()
+
+    # New
+    best_params = optimizer.solve()
+
+**TypeError: unexpected keyword argument 'optimizer'**
+
+Optimizer parameters are now passed to the constructor:
+
+.. code-block:: python
+
+    # Old
+    hyper.add_search(model, space, optimizer=opt, n_iter=100)
+
+    # New
+    optimizer = HillClimbing(
+        search_space=space,
+        n_iter=100,
+        experiment=model,
+    )
+
+
+Getting Help
+------------
+
+If you encounter issues migrating:
+
+1. Check the :ref:`api_reference` for current API
+2. See :ref:`examples` for v5 code examples
+3. Open an issue on `GitHub <https://github.com/SimonBlanke/Hyperactive/issues>`_
+4. Ask on `Discord <https://discord.gg/7uKdHfdcJG>`_
diff --git a/docs/source/user_guide/optimizers.rst b/docs/source/user_guide/optimizers.rst
new file mode 100644
index 00000000..748b393c
--- /dev/null
+++ b/docs/source/user_guide/optimizers.rst
@@ -0,0 +1,325 @@
+.. _user_guide_optimizers:
+
+==========
+Optimizers
+==========
+
+Optimizers define *how* Hyperactive explores the search space to find optimal parameters.
+This guide helps you choose the right optimizer for your problem and configure it effectively.
+
+
+Choosing an Optimizer
+---------------------
+
+The best optimizer depends on your problem characteristics:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 25 25 50
+
+   * - Scenario
+     - Recommended Optimizers
+     - Why
+   * - Quick baseline
+     - ``HillClimbing``, ``RandomSearch``
+     - Fast, simple, good for initial exploration
+   * - Expensive evaluations
+     - ``BayesianOptimizer``, ``TPEOptimizer``
+     - Learn from past evaluations, minimize function calls
+   * - Large search space
+     - ``RandomSearch``, ``ParticleSwarmOptimizer``
+     - Good global coverage
+   * - Multi-modal landscape
+     - ``GeneticAlgorithm``, ``DifferentialEvolution``
+     - Population-based, avoid local optima
+   * - Small search space
+     - ``GridSearch``
+     - Exhaustive coverage when feasible
+
+
+Optimizer Categories
+--------------------
+
+Hyperactive organizes optimizers into categories based on their search strategies.
+
+
+Local Search
+^^^^^^^^^^^^
+
+Local search optimizers explore the neighborhood of the current best solution.
+They're fast but may get stuck in local optima.
+
+**Hill Climbing**
+
+The simplest local search: always move to a better neighbor.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:hill_climbing]
+   :end-before: # [end:hill_climbing]
+
+**Simulated Annealing**
+
+Like hill climbing, but sometimes accepts worse solutions to escape local optima.
+The "temperature" controls exploration vs exploitation.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:simulated_annealing]
+   :end-before: # [end:simulated_annealing]
+
+**Repulsing Hill Climbing**
+
+Remembers visited regions and avoids them, encouraging broader exploration.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:repulsing_hill_climbing]
+   :end-before: # [end:repulsing_hill_climbing]
+
+**Stochastic Hill Climbing**
+
+Hill climbing with a probability of accepting worse solutions. The ``p_accept``
+parameter controls exploration — higher values make it more likely to accept
+non-improving moves, helping escape local optima.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:stochastic_hill_climbing]
+   :end-before: # [end:stochastic_hill_climbing]
+
+**Downhill Simplex (Nelder-Mead)**
+
+Uses a simplex of points to navigate the search space. Good for continuous problems.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:downhill_simplex]
+   :end-before: # [end:downhill_simplex]
+
+
+Global Search
+^^^^^^^^^^^^^
+
+Global search optimizers explore the entire search space more thoroughly.
+
+**Random Search**
+
+Samples random points from the search space. Simple but surprisingly effective baseline.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:random_search]
+   :end-before: # [end:random_search]
+
+**Grid Search**
+
+Evaluates all combinations systematically. Only practical for small search spaces.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:grid_search]
+   :end-before: # [end:grid_search]
+
+**Random Restart Hill Climbing**
+
+Runs hill climbing from multiple random starting points.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:random_restart_hill_climbing]
+   :end-before: # [end:random_restart_hill_climbing]
+
+**Powell's Method** and **Pattern Search**
+
+Classical derivative-free optimization methods.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:powells_pattern]
+   :end-before: # [end:powells_pattern]
+
+
+Population Methods
+^^^^^^^^^^^^^^^^^^
+
+Population-based optimizers maintain multiple candidate solutions that evolve together.
+They're excellent for complex, multi-modal optimization landscapes.
+
+**Particle Swarm Optimization**
+
+Particles "fly" through the search space, influenced by their own best position
+and the swarm's best position.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:particle_swarm]
+   :end-before: # [end:particle_swarm]
+
+**Genetic Algorithm**
+
+Evolves a population using selection, crossover, and mutation inspired by biology.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:genetic_algorithm]
+   :end-before: # [end:genetic_algorithm]
+
+**Evolution Strategy**
+
+Similar to genetic algorithms but focused on real-valued optimization.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:evolution_strategy]
+   :end-before: # [end:evolution_strategy]
+
+**Differential Evolution**
+
+Uses vector differences to guide mutation. Excellent for continuous optimization.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:differential_evolution]
+   :end-before: # [end:differential_evolution]
+
+**Parallel Tempering**
+
+Runs multiple chains at different "temperatures" and exchanges information between them.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:parallel_tempering]
+   :end-before: # [end:parallel_tempering]
+
+**Spiral Optimization**
+
+Particles spiral toward the best solution found so far.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:spiral_optimization]
+   :end-before: # [end:spiral_optimization]
+
+
+Sequential Model-Based (Bayesian)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+These optimizers build a model of the objective function and use it to decide
+where to sample next. Best for expensive evaluations.
+
+**Bayesian Optimization**
+
+Uses Gaussian Process regression to model the objective and acquisition functions
+to balance exploration and exploitation.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:bayesian_optimizer]
+   :end-before: # [end:bayesian_optimizer]
+
+**Tree-Structured Parzen Estimators (TPE)**
+
+Models the distribution of good and bad parameters separately.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:tpe]
+   :end-before: # [end:tpe]
+
+**Forest Optimizer**
+
+Uses Random Forest to model the objective function.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:forest_optimizer]
+   :end-before: # [end:forest_optimizer]
+
+**Lipschitz Optimization** and **DIRECT Algorithm**
+
+Use Lipschitz continuity assumptions to guide the search.
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:lipschitz_direct]
+   :end-before: # [end:lipschitz_direct]
+
+
+Optuna Backend
+^^^^^^^^^^^^^^
+
+Hyperactive provides wrappers for Optuna's powerful samplers:
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:optuna_imports]
+   :end-before: # [end:optuna_imports]
+
+Example with Optuna TPE:
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:optuna_tpe]
+   :end-before: # [end:optuna_tpe]
+
+
+Optimizer Configuration
+-----------------------
+
+Common Parameters
+^^^^^^^^^^^^^^^^^
+
+All optimizers accept these parameters:
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:common_parameters]
+   :end-before: # [end:common_parameters]
+
+
+Initialization Strategies
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Control how the optimizer initializes its search:
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:warm_start_example]
+   :end-before: # [end:warm_start_example]
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:initialization_strategies]
+   :end-before: # [end:initialization_strategies]
+
+
+Algorithm-Specific Parameters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Many optimizers have additional parameters. Check the :ref:`api_reference` for details.
+
+Example with Simulated Annealing:
+
+.. literalinclude:: ../_snippets/user_guide/optimizers.py
+   :language: python
+   :start-after: # [start:simulated_annealing_config]
+   :end-before: # [end:simulated_annealing_config]
+
+
+Performance Tips
+----------------
+
+1. **Start with baselines**: Always run ``RandomSearch`` first to establish
+   a baseline and understand your objective landscape.
+
+2. **Match iterations to complexity**: Complex optimizers (Bayesian, population-based)
+   need more iterations to show their advantages.
+
+3. **Consider evaluation cost**: For cheap evaluations, simple optimizers work well.
+   For expensive ones, use model-based approaches.
+
+4. **Use warm starts**: If you have prior knowledge, warm start can significantly
+   speed up optimization.
+
+5. **Set random seeds**: For reproducible results, always set ``random_state``.
diff --git a/docs/source/user_guide/search_spaces.rst b/docs/source/user_guide/search_spaces.rst
new file mode 100644
index 00000000..ee7fbb4a
--- /dev/null
+++ b/docs/source/user_guide/search_spaces.rst
@@ -0,0 +1,355 @@
+.. _user_guide_search_spaces:
+
+=========================
+Search Space Best Practices
+=========================
+
+This guide covers how to design effective search spaces for hyperparameter optimization.
+A well-designed search space can significantly improve optimization results and efficiency.
+
+
+Understanding Search Spaces
+---------------------------
+
+A search space defines the possible values for each parameter. Hyperactive samples
+from these values during optimization. The quality of your search space directly
+affects:
+
+- **Optimization speed**: Smaller, targeted spaces converge faster
+- **Solution quality**: Including good values is essential for finding them
+- **Memory usage**: Very large spaces can cause memory issues with some optimizers
+
+
+Defining Search Spaces
+----------------------
+
+Basic Structure
+^^^^^^^^^^^^^^^
+
+Search spaces are Python dictionaries mapping parameter names to lists of possible values:
+
+.. code-block:: python
+
+    search_space = {
+        "n_estimators": [10, 50, 100, 200],
+        "max_depth": [3, 5, 10, None],
+        "min_samples_split": [2, 5, 10],
+    }
+
+
+Discrete Values
+^^^^^^^^^^^^^^^
+
+For parameters with a small set of distinct values:
+
+.. code-block:: python
+
+    search_space = {
+        # Categorical choices
+        "kernel": ["linear", "rbf", "poly"],
+
+        # Boolean flags
+        "fit_intercept": [True, False],
+
+        # Specific integer values
+        "n_neighbors": [3, 5, 7, 9, 11],
+    }
+
+
+Continuous Ranges
+^^^^^^^^^^^^^^^^^
+
+For parameters that vary continuously, use NumPy to create arrays:
+
+.. code-block:: python
+
+    import numpy as np
+
+    search_space = {
+        # Linear spacing for uniform ranges
+        "momentum": np.linspace(0.5, 0.99, 50).tolist(),
+
+        # Log spacing for parameters spanning orders of magnitude
+        "learning_rate": np.logspace(-4, -1, 50).tolist(),
+
+        # Integer range
+        "hidden_size": list(range(32, 257, 32)),  # 32, 64, 96, ...
+    }
+
+.. tip::
+
+    Convert NumPy arrays to lists with ``.tolist()`` for cleaner code,
+    though Hyperactive accepts both formats.
+
+
+Scale-Appropriate Spacing
+-------------------------
+
+Linear vs Logarithmic
+^^^^^^^^^^^^^^^^^^^^^
+
+The spacing between values should match how the parameter affects your objective:
+
+**Linear spacing** — When changes have proportional effects:
+
+.. code-block:: python
+
+    # Dropout rate: 0.1 → 0.2 has similar effect as 0.5 → 0.6
+    "dropout": np.linspace(0.0, 0.5, 11).tolist()
+
+**Logarithmic spacing** — When the parameter spans orders of magnitude:
+
+.. code-block:: python
+
+    # Learning rate: 0.001 → 0.01 is as significant as 0.01 → 0.1
+    "learning_rate": np.logspace(-4, -1, 20).tolist()
+
+Common parameters that benefit from log spacing:
+
+- Learning rates (``1e-5`` to ``1e-1``)
+- Regularization strength (``1e-6`` to ``1e1``)
+- Batch sizes (powers of 2: 16, 32, 64, 128, ...)
+
+
+Choosing Granularity
+--------------------
+
+The number of values per parameter affects the total search space size:
+
+.. code-block:: python
+
+    # Fine granularity: 100 values per parameter
+    # 3 parameters → 100^3 = 1,000,000 combinations
+    "param_a": np.linspace(0, 1, 100).tolist(),
+    "param_b": np.linspace(0, 1, 100).tolist(),
+    "param_c": np.linspace(0, 1, 100).tolist(),
+
+    # Coarse granularity: 10 values per parameter
+    # 3 parameters → 10^3 = 1,000 combinations
+    "param_a": np.linspace(0, 1, 10).tolist(),
+    "param_b": np.linspace(0, 1, 10).tolist(),
+    "param_c": np.linspace(0, 1, 10).tolist(),
+
+**Guidelines:**
+
+- Start coarse, refine after initial results
+- Use finer granularity for sensitive parameters
+- Use coarser granularity for less important parameters
+
+
+Search Space Size Considerations
+--------------------------------
+
+Calculate your total search space size:
+
+.. code-block:: python
+
+    from functools import reduce
+    import operator
+
+    search_space = {
+        "n_estimators": [10, 50, 100, 200],        # 4 values
+        "max_depth": [3, 5, 10, None],              # 4 values
+        "learning_rate": np.logspace(-3, 0, 20),   # 20 values
+    }
+
+    total_combinations = reduce(
+        operator.mul,
+        [len(v) for v in search_space.values()]
+    )
+    print(f"Total combinations: {total_combinations:,}")  # 320
+
+**Recommendations by search space size:**
+
+.. list-table::
+   :header-rows: 1
+   :widths: 25 35 40
+
+   * - Size
+     - Recommended Approach
+     - Optimizer Suggestions
+   * - <100
+     - Grid search (exhaustive)
+     - ``GridSearch``
+   * - 100–10,000
+     - Random or local search
+     - ``RandomSearch``, ``HillClimbing``
+   * - 10,000–1,000,000
+     - Smart sampling required
+     - ``BayesianOptimizer``, ``TPE``
+   * - >1,000,000
+     - Reduce search space or use population methods
+     - ``ParticleSwarmOptimizer``, ``EvolutionStrategy``
+
+
+Handling Parameter Dependencies
+-------------------------------
+
+Sometimes parameters have dependencies. Handle these in your objective function:
+
+.. code-block:: python
+
+    def objective(params):
+        # Constraint: min_samples_split >= min_samples_leaf
+        if params["min_samples_split"] < params["min_samples_leaf"]:
+            return -np.inf  # Invalid configuration
+
+        # Constraint: kernel-specific parameters
+        if params["kernel"] != "poly" and params["degree"] != 3:
+            return -np.inf  # degree only relevant for poly kernel
+
+        # Valid configuration — proceed with evaluation
+        return evaluate_model(params)
+
+.. note::
+
+    Returning ``-np.inf`` effectively removes invalid combinations from consideration.
+
+
+Common Search Space Patterns
+----------------------------
+
+Scikit-learn Classifiers
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: python
+
+    # Random Forest
+    rf_space = {
+        "n_estimators": [50, 100, 200, 500],
+        "max_depth": [None, 5, 10, 20, 30],
+        "min_samples_split": [2, 5, 10],
+        "min_samples_leaf": [1, 2, 4],
+        "max_features": ["sqrt", "log2", None],
+    }
+
+    # Gradient Boosting
+    gb_space = {
+        "n_estimators": [50, 100, 200],
+        "learning_rate": np.logspace(-3, 0, 10).tolist(),
+        "max_depth": [3, 5, 7, 9],
+        "subsample": np.linspace(0.6, 1.0, 5).tolist(),
+    }
+
+    # SVM
+    svm_space = {
+        "C": np.logspace(-2, 2, 10).tolist(),
+        "gamma": np.logspace(-4, -1, 10).tolist(),
+        "kernel": ["rbf", "poly", "sigmoid"],
+    }
+
+
+Neural Networks
+^^^^^^^^^^^^^^^
+
+.. code-block:: python
+
+    nn_space = {
+        "hidden_layers": [1, 2, 3],
+        "hidden_size": [32, 64, 128, 256],
+        "learning_rate": np.logspace(-4, -2, 20).tolist(),
+        "dropout": np.linspace(0.0, 0.5, 6).tolist(),
+        "batch_size": [16, 32, 64, 128],
+        "activation": ["relu", "tanh", "elu"],
+    }
+
+
+Iterative Refinement Strategy
+-----------------------------
+
+A practical approach for finding optimal hyperparameters:
+
+**Phase 1: Coarse Search**
+
+.. code-block:: python
+
+    # Wide ranges, few values
+    coarse_space = {
+        "learning_rate": [1e-4, 1e-3, 1e-2, 1e-1],
+        "hidden_size": [32, 128, 512],
+        "dropout": [0.0, 0.25, 0.5],
+    }
+
+    optimizer = RandomSearch(
+        search_space=coarse_space,
+        n_iter=50,
+        experiment=objective,
+    )
+    best = optimizer.solve()
+    # Result: learning_rate=1e-3 works best
+
+**Phase 2: Fine-tune Around Best Values**
+
+.. code-block:: python
+
+    # Narrow ranges around best from phase 1
+    fine_space = {
+        "learning_rate": np.logspace(-3.5, -2.5, 20).tolist(),  # Around 1e-3
+        "hidden_size": list(range(96, 192, 16)),                # Around 128
+        "dropout": np.linspace(0.2, 0.4, 10).tolist(),         # Around 0.25
+    }
+
+    optimizer = BayesianOptimizer(
+        search_space=fine_space,
+        n_iter=100,
+        experiment=objective,
+    )
+    final_best = optimizer.solve()
+
+
+Common Mistakes to Avoid
+------------------------
+
+**1. Overly Large Search Spaces**
+
+.. code-block:: python
+
+    # Bad: 1000 * 1000 * 1000 = 1 billion combinations
+    bad_space = {
+        "param_a": np.linspace(0, 1, 1000).tolist(),
+        "param_b": np.linspace(0, 1, 1000).tolist(),
+        "param_c": np.linspace(0, 1, 1000).tolist(),
+    }
+
+    # Better: 50 * 50 * 50 = 125,000 combinations
+    better_space = {
+        "param_a": np.linspace(0, 1, 50).tolist(),
+        "param_b": np.linspace(0, 1, 50).tolist(),
+        "param_c": np.linspace(0, 1, 50).tolist(),
+    }
+
+**2. Linear Spacing for Log-Scale Parameters**
+
+.. code-block:: python
+
+    # Bad: most values clustered at high end
+    bad_lr = np.linspace(0.0001, 0.1, 20).tolist()
+    # Values: 0.0001, 0.0053, 0.0106, ... (poor coverage of small values)
+
+    # Good: even distribution across magnitudes
+    good_lr = np.logspace(-4, -1, 20).tolist()
+    # Values: 0.0001, 0.00016, 0.00025, ... 0.063, 0.1
+
+**3. Missing Important Values**
+
+.. code-block:: python
+
+    # Bad: might miss optimal region entirely
+    bad_space = {"max_depth": [2, 3, 4]}
+
+    # Better: include None and reasonable range
+    better_space = {"max_depth": [None, 3, 5, 10, 20, 50]}
+
+**4. Ignoring Parameter Interactions**
+
+Some parameters interact strongly. Consider them together:
+
+.. code-block:: python
+
+    # Learning rate and batch size often interact
+    # Higher batch sizes often need higher learning rates
+    search_space = {
+        "batch_size": [16, 32, 64, 128],
+        "learning_rate": np.logspace(-4, -1, 20).tolist(),
+    }
+    # The optimizer will explore combinations to find the best pairing
diff --git a/docs/tests/__init__.py b/docs/tests/__init__.py
new file mode 100644
index 00000000..c5de4959
--- /dev/null
+++ b/docs/tests/__init__.py
@@ -0,0 +1 @@
+"""Documentation tests package."""
diff --git a/docs/tests/test_doc_snippets.py b/docs/tests/test_doc_snippets.py
new file mode 100644
index 00000000..0a7ab2e9
--- /dev/null
+++ b/docs/tests/test_doc_snippets.py
@@ -0,0 +1,270 @@
+"""Test all documentation code snippets.
+
+This module discovers and tests all Python snippet files in the documentation.
+Only snippets in the 'getting_started/' directory are tested for execution,
+as they contain complete, runnable examples.
+
+User guide snippets may contain illustrative code with placeholders and are
+not required to be executable - they serve documentation purposes.
+"""
+
+import importlib.util
+import re
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+# Path to the snippets directory
+SNIPPETS_DIR = Path(__file__).parent.parent / "source" / "_snippets"
+# Path to the docs source directory
+SOURCE_DIR = Path(__file__).parent.parent / "source"
+# Path to the repository root
+REPO_ROOT = Path(__file__).parent.parent.parent
+
+
+def get_testable_snippet_files():
+    """Collect Python files in testable directories.
+
+    Only includes files from directories that contain complete, runnable examples.
+    Currently: getting_started/, examples/, installation/
+
+    Returns
+    -------
+    list[Path]
+        List of paths to testable Python snippet files.
+    """
+    testable_dirs = ["getting_started", "examples", "installation"]
+    snippet_files = []
+
+    for dir_name in testable_dirs:
+        dir_path = SNIPPETS_DIR / dir_name
+        if dir_path.exists():
+            for path in dir_path.rglob("*.py"):
+                if path.name not in ("__init__.py", "conftest.py"):
+                    snippet_files.append(path)
+
+    return sorted(snippet_files)
+
+
+def get_all_snippet_files():
+    """Collect all Python files in the snippets directory.
+
+    Returns
+    -------
+    list[Path]
+        List of paths to all Python snippet files.
+    """
+    snippet_files = []
+    for path in SNIPPETS_DIR.rglob("*.py"):
+        if path.name not in ("__init__.py", "conftest.py"):
+            snippet_files.append(path)
+    return sorted(snippet_files)
+
+
+def _snippet_id(path):
+    """Generate a readable test ID for a snippet file."""
+    return str(path.relative_to(SNIPPETS_DIR))
+
+
+@pytest.mark.parametrize("snippet_file", get_testable_snippet_files(), ids=_snippet_id)
+def test_snippet_executes(snippet_file):
+    """Test that each runnable snippet file executes without errors.
+
+    This runs each snippet as a subprocess to ensure isolation between tests
+    and to catch any import-time errors.
+
+    Parameters
+    ----------
+    snippet_file : Path
+        Path to the snippet file to test.
+    """
+    result = subprocess.run(
+        [sys.executable, str(snippet_file)],
+        capture_output=True,
+        text=True,
+        timeout=120,  # 2 minute timeout for optimization examples
+        cwd=str(SNIPPETS_DIR),
+    )
+
+    # Provide helpful error message on failure
+    if result.returncode != 0:
+        error_msg = f"Snippet {snippet_file.name} failed to execute.\n"
+        error_msg += f"stdout:\n{result.stdout}\n"
+        error_msg += f"stderr:\n{result.stderr}"
+        pytest.fail(error_msg)
+
+
+@pytest.mark.parametrize("snippet_file", get_testable_snippet_files(), ids=_snippet_id)
+def test_snippet_imports(snippet_file):
+    """Test that each runnable snippet file can be imported as a module.
+
+    This catches syntax errors and import-time errors in a more controlled way.
+
+    Parameters
+    ----------
+    snippet_file : Path
+        Path to the snippet file to test.
+    """
+    spec = importlib.util.spec_from_file_location(
+        f"snippet_{snippet_file.stem}", snippet_file
+    )
+    if spec is None or spec.loader is None:
+        pytest.fail(f"Could not load spec for {snippet_file}")
+
+    module = importlib.util.module_from_spec(spec)
+    try:
+        spec.loader.exec_module(module)
+    except Exception as e:
+        pytest.fail(f"Snippet {snippet_file.name} failed to import: {e}")
+
+
+def test_all_snippets_have_markers():
+    """Test that all snippet files contain proper start/end markers.
+
+    This ensures that literalinclude directives can extract code properly.
+    """
+    for snippet_file in get_all_snippet_files():
+        content = snippet_file.read_text()
+
+        # Check for at least one start/end pair
+        has_start = "# [start:" in content
+        has_end = "# [end:" in content
+
+        if not (has_start and has_end):
+            pytest.fail(
+                f"Snippet {snippet_file.name} missing start/end markers. "
+                f"has_start={has_start}, has_end={has_end}"
+            )
+
+
+def test_snippet_markers_are_balanced():
+    """Test that start/end markers are properly paired in each snippet file."""
+    import re
+
+    for snippet_file in get_all_snippet_files():
+        content = snippet_file.read_text()
+
+        starts = re.findall(r"# \[start:(\w+)\]", content)
+        ends = re.findall(r"# \[end:(\w+)\]", content)
+
+        # Check that every start has a matching end
+        for marker in starts:
+            if marker not in ends:
+                pytest.fail(
+                    f"Snippet {snippet_file.name} has unmatched start marker: {marker}"
+                )
+
+        for marker in ends:
+            if marker not in starts:
+                pytest.fail(
+                    f"Snippet {snippet_file.name} has unmatched end marker: {marker}"
+                )
+
+
+def get_rst_files():
+    """Collect all RST files in the source directory.
+
+    Returns
+    -------
+    list[Path]
+        List of paths to all RST files.
+    """
+    return sorted(SOURCE_DIR.rglob("*.rst"))
+
+
+def extract_github_file_links(content: str) -> list[tuple[str, str]]:
+    """Extract GitHub file links from RST content.
+
+    Finds links of the form:
+    https://github.com/SimonBlanke/Hyperactive/blob/master/path/to/file.py
+
+    Parameters
+    ----------
+    content : str
+        RST file content.
+
+    Returns
+    -------
+    list[tuple[str, str]]
+        List of (full_url, relative_path) tuples.
+    """
+    # Pattern matches GitHub blob URLs to this repo
+    pattern = r"https://github\.com/SimonBlanke/Hyperactive/blob/master/([^\s>`\"\']+)"
+    matches = re.findall(pattern, content)
+    return [
+        (f"https://github.com/SimonBlanke/Hyperactive/blob/master/{path}", path)
+        for path in matches
+    ]
+
+
+def test_github_example_links_exist():
+    """Test that all GitHub example links in RST files point to existing files.
+
+    This verifies that documentation links to example files are not broken.
+    Only checks links to files within this repository.
+    """
+    broken_links = []
+
+    for rst_file in get_rst_files():
+        content = rst_file.read_text()
+        links = extract_github_file_links(content)
+
+        for full_url, rel_path in links:
+            local_path = REPO_ROOT / rel_path
+            if not local_path.exists():
+                broken_links.append(f"{rst_file.name}: {rel_path} (file not found)")
+
+    if broken_links:
+        msg = f"Found {len(broken_links)} broken GitHub file link(s):\n"
+        msg += "\n".join(f"  - {link}" for link in broken_links)
+        pytest.fail(msg)
+
+
+def extract_include_paths(content: str) -> list[str]:
+    """Extract include and literalinclude paths from RST content.
+
+    Parameters
+    ----------
+    content : str
+        RST file content.
+
+    Returns
+    -------
+    list[str]
+        List of relative paths referenced by include directives.
+    """
+    # Match both include and literalinclude directives
+    # Format: .. include:: path or .. literalinclude:: path
+    pattern = r"\.\.\s+(?:include|literalinclude)::\s+([^\s\n]+)"
+    return re.findall(pattern, content)
+
+
+def test_rst_includes_exist():
+    """Test that all include/literalinclude paths in RST files exist.
+
+    This catches broken include directives that reference non-existent files.
+    """
+    broken_includes = []
+
+    for rst_file in get_rst_files():
+        # Skip auto-generated files and templates (they use Jinja placeholders)
+        if "auto_generated" in str(rst_file) or "_templates" in str(rst_file):
+            continue
+
+        content = rst_file.read_text()
+        includes = extract_include_paths(content)
+
+        for include_path in includes:
+            # Resolve path relative to the RST file's directory
+            full_path = rst_file.parent / include_path
+            if not full_path.exists():
+                broken_includes.append(
+                    f"{rst_file.relative_to(SOURCE_DIR)}: {include_path}"
+                )
+
+    if broken_includes:
+        msg = f"Found {len(broken_includes)} broken include path(s):\n"
+        msg += "\n".join(f"  - {inc}" for inc in broken_includes)
+        pytest.fail(msg)
diff --git a/pyproject.toml b/pyproject.toml
index a11b720e..b7d5c6c1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -59,7 +59,7 @@ build = [
   "wheel",
 ]
 test = [
-  "pytest == 9.0.1",
+  "pytest == 9.0.2",
   "flake8",
   "pytest-cov",
   "pathos",
@@ -71,9 +71,19 @@ test_parallel_backends = [
   "joblib",
   'ray >=2.40.0; python_version < "3.13"',
 ]
+docs = [
+  "myst-parser",
+  "numpydoc",
+  "pydata-sphinx-theme",
+  "Sphinx>=7.0.0,<9.0.0",
+  "sphinx-copybutton",
+  "sphinx-design",
+  "sphinx-issues",
+]
 all_extras = [
   "hyperactive[integrations]",
   "optuna<5",
+  "cmaes",  # Required for CmaEsOptimizer (optuna's CMA-ES sampler)
   "lightning",
 ]
 
diff --git a/requirements/requirements-test.in b/requirements/requirements-test.in
index 9b22ebe5..b9ce6794 100644
--- a/requirements/requirements-test.in
+++ b/requirements/requirements-test.in
@@ -1,4 +1,4 @@
-pytest == 9.0.1
+pytest == 9.0.2
 flake8
 pytest-cov
 pathos
diff --git a/src/hyperactive/integrations/sklearn/opt_cv.py b/src/hyperactive/integrations/sklearn/opt_cv.py
index 2f0366fe..aae6ffc0 100644
--- a/src/hyperactive/integrations/sklearn/opt_cv.py
+++ b/src/hyperactive/integrations/sklearn/opt_cv.py
@@ -1,7 +1,6 @@
 """opt_cv module for Hyperactive optimization."""
 
 from collections.abc import Callable
-from typing import Union
 
 from sklearn.base import BaseEstimator, clone
 
@@ -107,7 +106,7 @@ def __init__(
         estimator,
         optimizer,
         *,
-        scoring: Union[Callable, str, None] = None,
+        scoring: Callable | str | None = None,
         refit: bool = True,
         cv=None,
     ):
diff --git a/src/hyperactive/opt/optuna/_cmaes_optimizer.py b/src/hyperactive/opt/optuna/_cmaes_optimizer.py
index 4e47f0ec..3c3c5788 100644
--- a/src/hyperactive/opt/optuna/_cmaes_optimizer.py
+++ b/src/hyperactive/opt/optuna/_cmaes_optimizer.py
@@ -128,6 +128,7 @@ def get_test_params(cls, parameter_set="default"):
         from sklearn.datasets import make_regression
         from sklearn.neural_network import MLPRegressor
 
+        from hyperactive.experiment.bench import Sphere
         from hyperactive.experiment.integrations import SklearnCvExperiment
 
         # Test case 1: Basic continuous parameters (from base)
@@ -165,6 +166,8 @@ def get_test_params(cls, parameter_set="default"):
         )
 
         # Test case 3: High-dimensional continuous space (CMA-ES strength)
+        # Use Sphere benchmark which expects x0, x1, ..., xN params
+        sphere_exp = Sphere(n_dim=6)
         high_dim_continuous = {
             f"x{i}": (-1.0, 1.0)
             for i in range(6)  # 6D continuous optimization
@@ -174,7 +177,7 @@ def get_test_params(cls, parameter_set="default"):
             {
                 "param_space": high_dim_continuous,
                 "n_trials": 12,
-                "experiment": mlp_exp,
+                "experiment": sphere_exp,
                 "sigma0": 0.7,  # Larger initial spread
                 "n_startup_trials": 3,
             }