# optuna.multi_objective.samplers.NSGAIIMultiObjectiveSampler¶

class optuna.multi_objective.samplers.NSGAIIMultiObjectiveSampler(*args, **kwargs)[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.

Parameters
• population_size – Number of individuals (trials) in a generation.

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

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

• seed – Seed for random number generator.

Note

Added in v1.5.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v1.5.0.

__init__(population_size: int = 50, mutation_prob: Optional[float] = None, crossover_prob: float = 0.9, swapping_prob: float = 0.5, seed: Optional[int] = None)None[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

 __init__([population_size, mutation_prob, …]) Initialize self. infer_relative_search_space(study, trial) Infer the search space that will be used by relative sampling in the target trial. 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.
infer_relative_search_space(study: optuna.multi_objective.study.MultiObjectiveStudy, trial: optuna.multi_objective.trial.FrozenMultiObjectiveTrial) → Dict[str, optuna.distributions.BaseDistribution][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 – Target study object.

• trial – Target trial object.

Returns

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

sample_independent(study: optuna.multi_objective.study.MultiObjectiveStudy, trial: optuna.multi_objective.trial.FrozenMultiObjectiveTrial, param_name: str, param_distribution: optuna.distributions.BaseDistribution) → Any[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 the relationship between parameters such as random sampling.

Parameters
• study – Target study object.

• trial – Target trial object.

• param_name – Name of the sampled parameter.

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

Returns

A parameter value.

sample_relative(study: optuna.multi_objective.study.MultiObjectiveStudy, trial: optuna.multi_objective.trial.FrozenMultiObjectiveTrial, search_space: Dict[str, optuna.distributions.BaseDistribution]) → Dict[str, Any][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 the relationship between parameters.

Parameters
Returns

A dictionary containing the parameter names and the values.