Implement NSGA-III using BaseGASampler#6219
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gen740 merged 2 commits intooptuna:masterfrom Aug 1, 2025
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BaseGASampler
nabenabe0928
approved these changes
Jul 28, 2025
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@gen740 Could you review this PR? |
gen740
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Aug 1, 2025
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I confirmed that this PR returns identical results as in the master branch using the following code.
import optuna
from optuna.samplers import NSGAIIISampler
def objective(trial):
x = trial.suggest_float("x", -10, 10)
y = trial.suggest_float("y", -10, 10)
return (x - 2) ** 2 + (y - 3) ** 2, (x + 2) ** 2 + (y + 3) ** 2
study = optuna.create_study(
directions=["minimize", "minimize"],
sampler=NSGAIIISampler(seed=42),
)
study.optimize(objective, n_trials=30)LGTM!
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Motivation & Description of the changes
This PR implements NSGAIII using BaseGASampler as in #5864.
This change reduces code duplication. It also eliminates some unintuitive internal differences between NSGA-II and NSGA-III.
For example, in Optuna v4.4, _GENERATION_KEY is not defined in NSGA-III, as shown below, but it is now defined.