optuna.samplers.NSGAIISampler

class optuna.samplers.NSGAIISampler(*, population_size=50, mutation_prob=None, crossover=None, crossover_prob=0.9, swapping_prob=0.5, seed=None, constraints_func=None, elite_population_selection_strategy=None, child_generation_strategy=None, after_trial_strategy=None)[source]

Multi-objective sampler using the NSGA-II algorithm.

NSGA-II stands for “Nondominated Sorting Genetic Algorithm II”, which is a well known, fast and elitist multi-objective genetic algorithm.

For further information about NSGA-II, please refer to the following paper:

Note

TPESampler became much faster in v4.0.0 and supports several features not supported by NSGAIISampler such as handling of dynamic search space and categorical distance. To use TPESampler, you need to explicitly specify the sampler as follows:

import optuna


def objective(trial):
    x = trial.suggest_float("x", -100, 100)
    y = trial.suggest_categorical("y", [-1, 0, 1])
    f1 = x**2 + y
    f2 = -((x - 2) ** 2 + y)
    return f1, f2


# We minimize the first objective and maximize the second objective.
sampler = optuna.samplers.TPESampler()
study = optuna.create_study(directions=["minimize", "maximize"], sampler=sampler)
study.optimize(objective, n_trials=100)

Please also check our article for more details of the speedup in v4.0.0.

Parameters:

  • population_size (int) – Number of individuals (trials) in a generation. population_size must be greater than or equal to crossover.n_parents. For UNDXCrossover and SPXCrossover, n_parents=3, and for the other algorithms, n_parents=2.

  • mutation_prob (float | None) – Probability of mutating each parameter when creating a new individual. If None is specified, the value 1.0 / len(parent_trial.params) is used where parent_trial is the parent trial of the target individual.

  • crossover (BaseCrossover | None) –

    Crossover to be applied when creating child individuals. The available crossovers are listed here: https://optuna.readthedocs.io/en/stable/reference/samplers/nsgaii.html.

    UniformCrossover is always applied to parameters sampled from CategoricalDistribution, and by default for parameters sampled from other distributions unless this argument is specified.

    For more information on each of the crossover method, please refer to specific crossover documentation.

  • crossover_prob (float) – Probability that a crossover (parameters swapping between parents) will occur when creating a new individual.

  • swapping_prob (float) – Probability of swapping each parameter of the parents during crossover.

  • seed (int | None) – Seed for random number generator.

  • constraints_func (Callable[[FrozenTrial], Sequence[float]] | None) –

    An optional function that computes the objective constraints. It must take a FrozenTrial and return the constraints. The return value must be a sequence of float s. A value strictly larger than 0 means that a constraints is violated. A value equal to or smaller than 0 is considered feasible. If constraints_func returns more than one value for a trial, that trial is considered feasible if and only if all values are equal to 0 or smaller.

    The constraints_func will be evaluated after each successful trial. The function won’t be called when trials fail or they are pruned, but this behavior is subject to change in the future releases.

    The constraints are handled by the constrained domination. A trial x is said to constrained-dominate a trial y, if any of the following conditions is true:

    1. Trial x is feasible and trial y is not.

    2. Trial x and y are both infeasible, but trial x has a smaller overall violation.

    3. Trial x and y are feasible and trial x dominates trial y.

    Note

    Added in v2.5.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.5.0.

  • elite_population_selection_strategy (Callable[[Study, list[FrozenTrial]], list[FrozenTrial]] | None) –

    The selection strategy for determining the individuals to survive from the current population pool. Default to None.

    Note

    The arguments elite_population_selection_strategy was added in v3.3.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.3.0.

  • child_generation_strategy (Callable[[Study, dict[str, BaseDistribution], list[FrozenTrial]], dict[str, Any]] | None) –

    The strategy for generating child parameters from parent trials. Defaults to None.

    Note

    The arguments child_generation_strategy was added in v3.3.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.3.0.

  • after_trial_strategy (Callable[[Study, FrozenTrial, TrialState, Sequence[float] | None], None] | None) –

    A set of procedure to be conducted after each trial. Defaults to None.

    Note

    The arguments after_trial_strategy was added in v3.3.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.3.0.

Methods

after_trial(study, trial, state, values)

Trial post-processing.

before_trial(study, trial)

Trial pre-processing.

infer_relative_search_space(study, trial)

Infer the search space that will be used by relative sampling in the target trial.

reseed_rng()

Reseed sampler's random number generator.

sample_independent(study, trial, param_name, ...)

Sample a parameter for a given distribution.

sample_relative(study, trial, search_space)

Sample parameters in a given search space.
after_trial(study, trial, state, values)[source]

Trial post-processing.

This method is called after the objective function returns and right before the trial is finished and its state is stored.

Note

Added in v2.4.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.4.0.

Parameters:

  • study (Study) – Target study object.

  • trial (FrozenTrial) – Target trial object. Take a copy before modifying this object.

  • state (TrialState) – Resulting trial state.

  • values (Sequence[float] | None) – Resulting trial values. Guaranteed to not be None if trial succeeded.

Return type:

None

before_trial(study, trial)[source]

Trial pre-processing.

This method is called before the objective function is called and right after the trial is instantiated. More precisely, this method is called during trial initialization, just before the infer_relative_search_space() call. In other words, it is responsible for pre-processing that should be done before inferring the search space.

Note

Added in v3.3.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.3.0.

Parameters:

  • study (Study) – Target study object.

  • trial (FrozenTrial) – Target trial object.

Return type:

None

infer_relative_search_space(study, trial)[source]

Infer the search space that will be used by relative sampling in the target trial.

This method is called right before sample_relative() method, and the search space returned by this method is passed to it. The parameters not contained in the search space will be sampled by using sample_independent() method.

Parameters:

  • study (Study) – Target study object.

  • trial (FrozenTrial) – Target trial object. Take a copy before modifying this object.

Returns:

A dictionary containing the parameter names and parameter’s distributions.

Return type:

dict[str, BaseDistribution]

See also

Please refer to intersection_search_space() as an implementation of infer_relative_search_space().

reseed_rng()[source]

Reseed sampler’s random number generator.

This method is called by the Study instance if trials are executed in parallel with the option n_jobs>1. In that case, the sampler instance will be replicated including the state of the random number generator, and they may suggest the same values. To prevent this issue, this method assigns a different seed to each random number generator.

Return type:

None

sample_independent(study, trial, param_name, param_distribution)[source]

Sample a parameter for a given distribution.

This method is called only for the parameters not contained in the search space returned by sample_relative() method. This method is suitable for sampling algorithms that do not use relationship between parameters such as random sampling and TPE.

Note

The failed trials are ignored by any build-in samplers when they sample new parameters. Thus, failed trials are regarded as deleted in the samplers’ perspective.

Parameters:

  • study (Study) – Target study object.

  • trial (FrozenTrial) – Target trial object. Take a copy before modifying this object.

  • param_name (str) – Name of the sampled parameter.

  • param_distribution (BaseDistribution) – Distribution object that specifies a prior and/or scale of the sampling algorithm.

Returns:

A parameter value.

Return type:

Any

sample_relative(study, trial, search_space)[source]

Sample parameters in a given search space.

This method is called once at the beginning of each trial, i.e., right before the evaluation of the objective function. This method is suitable for sampling algorithms that use relationship between parameters such as Gaussian Process and CMA-ES.

Note

The failed trials are ignored by any build-in samplers when they sample new parameters. Thus, failed trials are regarded as deleted in the samplers’ perspective.

Parameters:
Returns:

A dictionary containing the parameter names and the values.

Return type:

dict[str, Any]