# optuna.multi_objective.samplers.NSGAIIMultiObjectiveSampler¶

class optuna.multi_objective.samplers.NSGAIIMultiObjectiveSampler(population_size=50, mutation_prob=None, crossover_prob=0.9, swapping_prob=0.5, seed=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:

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.

Warning

Deprecated in v2.4.0. This feature will be removed in the future. The removal of this feature is currently scheduled for v4.0.0, but this schedule is subject to change. See https://github.com/optuna/optuna/releases/tag/v2.4.0.

Methods

 infer_relative_search_space(study, trial) Infer the search space that will be used by relative sampling in the target trial. 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.
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
Returns

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

Return type

Dict[str, optuna.distributions.BaseDistribution]

reseed_rng()[source]

Reseed sampler’s random number generator.

This method is called by the MultiObjectiveStudy 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 the relationship between parameters such as random sampling.

Parameters
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 the relationship between parameters.

Parameters
Returns

A dictionary containing the parameter names and the values.

Return type

Dict[str, Any]