NSGA-II with initial trials

package/samplers/nsgaii_with_initial_trials/LICENSE

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+MIT License
+
+Copyright (c) 2024 <Hiroaki Natsume>
+
+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.

package/samplers/nsgaii_with_initial_trials/README.md

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+---
+author: Hiroaki Natsume
+title: NSGAII sampler with Initial Trials
+description: Sampler using NSGAII algorithm with initial trials.
+tags: [Sampler, Multi-Objective, Genetic Algorithm]
+optuna_versions: [4.0.0]
+license: MIT License
+---
+
+## Abstract
+
+If Optuna's built-in NSGAII has a study obtained from another sampler, but continues with that study, it cannot be used as the first generation, and optimization starts from zero.
+This means that even if you already know good individuals, you cannot use it in the GA.
+In this implementation, the already sampled results are included in the initial individuals of the GA to perform the optimization.
+
+Note, however, that this has the effect that the implementation does not necessarily support multi-threading in the generation of the initial generation.
+After the initial generation, the implementation is similar to the built-in NSGAII.
+
+## Class or Function Names
+
+- NSGAIIwITSampler
+
+## Example
+
+```python
+import optuna
+import optunahub
+
+
+def objective(trial: optuna.Trial) -> tuple[float, float]:
+    x = trial.suggest_float("x", 0, 5)
+    y = trial.suggest_float("y", 0, 3)
+    v0 = 4 * x**2 + 4 * y**2
+    v1 = (x - 5) ** 2 + (y - 5) ** 2
+    return v0, v1
+
+
+storage = optuna.storages.InMemoryStorage()
+
+# Sampling 0 generation using enqueueing & qmc sampler
+study = optuna.create_study(
+    directions=["minimize", "minimize"],
+    sampler=optuna.samplers.QMCSampler(seed=42),
+    study_name="test",
+    storage=storage,
+)
+study.enqueue_trial(
+    {
+        "x": 0,
+        "y": 0,
+    }
+)
+study.optimize(objective, n_trials=128)
+
+# Using sampling results as the initial generation
+sampler = optunahub.load_module(
+    "samplers/nsgaii_with_initial_trials",
+).NSGAIIwITSampler(population_size=25, seed=42)
+
+study = optuna.create_study(
+    directions=["minimize", "minimize"],
+    sampler=sampler,
+    study_name="test",
+    storage=storage,
+    load_if_exists=True,
+)
+study.optimize(objective, n_trials=100)
+
+optuna.visualization.plot_pareto_front(study).show()
+```
+
+## Others
+
+The implementation is similar to Optuna's NSGAII except for the handling of initial generations. The license and documentation are below.
+
+- [Documentation](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.NSGAIISampler.html)
+- [License](https://github.com/optuna/optuna/blob/master/LICENSE)

package/samplers/nsgaii_with_initial_trials/__init__.py

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+from .nsgaii_with_initial_trials import NSGAIIwITSampler
+
+
+__all__ = ["NSGAIIwITSampler"]

package/samplers/nsgaii_with_initial_trials/example.py

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+import optuna
+import optunahub
+
+
+def objective(trial: optuna.Trial) -> tuple[float, float]:
+    x = trial.suggest_float("x", 0, 5)
+    y = trial.suggest_float("y", 0, 3)
+    v0 = 4 * x**2 + 4 * y**2
+    v1 = (x - 5) ** 2 + (y - 5) ** 2
+    return v0, v1
+
+
+storage = optuna.storages.InMemoryStorage()
+# Sampling 0 generation using enqueueing & qmc sampler
+study = optuna.create_study(
+    directions=["minimize", "minimize"],
+    sampler=optuna.samplers.QMCSampler(seed=42),
+    study_name="test",
+    storage=storage,
+)
+study.enqueue_trial(
+    {
+        "x": 0,
+        "y": 0,
+    }
+)
+study.optimize(objective, n_trials=128)
+
+# Using previous sampling results as the initial generation,
+# sampled by NSGAII.
+sampler = optunahub.load_module(
+    "samplers/nsgaii_with_initial_trials",
+).NSGAIIwITSampler(population_size=25, seed=42)
+
+study = optuna.create_study(
+    directions=["minimize", "minimize"],
+    sampler=sampler,
+    study_name="test",
+    storage=storage,
+    load_if_exists=True,
+)
+study.optimize(objective, n_trials=100)
+
+optuna.visualization.plot_pareto_front(study).show()