# optuna.samplers.NSGAIISampler¶

class optuna.samplers.NSGAIISampler(*, population_size=50, mutation_prob=None, crossover_prob=0.9, swapping_prob=0.5, seed=None, constraints_func=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.

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.

• constraints_func

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 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.

Methods

 after_trial(study, trial, state, values) Trial post-processing. 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.
after_trial(study, trial, state, values)[source]

Trial post-processing.

This method is called after the objective function returns and right before the trials 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 (optuna.study.Study) – Target study object.

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

• state (optuna.trial._state.TrialState) – Resulting trial state.

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

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 (optuna.study.Study) – Target study object.

• trial (optuna.trial._frozen.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, optuna.distributions.BaseDistribution]

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 (optuna.study.Study) – Target study object.

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

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

• param_distribution (optuna.distributions.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]