optuna.samplers.NSGAIIISampler

class optuna.samplers.NSGAIIISampler(*, population_size=50, mutation_prob=None, crossover=None, crossover_prob=0.9, swapping_prob=0.5, seed=None, constraints_func=None, reference_points=None, dividing_parameter=3, elite_population_selection_strategy=None, child_generation_strategy=None, after_trial_strategy=None)

Multi-objective sampler using the NSGA-III algorithm.

NSGA-III stands for “Nondominated Sorting Genetic Algorithm III”, which is a modified version of NSGA-II for many objective optimization problem.

For further information about NSGA-III, please refer to the following papers:

• An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints

• An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach

Parameters:

• reference_points (np.ndarray | None) – A 2 dimension numpy.ndarray with objective dimension columns. Represents a list of reference points which is used to determine who to survive. After non-dominated sort, who out of borderline front are going to survived is determined according to how sparse the closest reference point of each individual is. In the default setting the algorithm uses uniformly spread points to diversify the result. It is also possible to reflect your preferences by giving an arbitrary set of target points since the algorithm prioritizes individuals around reference points.

• dividing_parameter (int) – A parameter to determine the density of default reference points. This parameter determines how many divisions are made between reference points on each axis. The smaller this value is, the less reference points you have. The default value is 3. Note that this parameter is not used when reference_points is not None.

• population_size (int)

• mutation_prob (float | None)

• crossover (BaseCrossover | None)

• crossover_prob (float)

• swapping_prob (float)

• seed (int | None)

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

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

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

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

Note

Other parameters than reference_points and dividing_parameter are the same as NSGAIISampler.

Note

Added in v3.2.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.2.0.

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)

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)

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)

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()

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)

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)

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:

• study (Study) – Target study object.

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

• search_space (dict[str, BaseDistribution]) – The search space returned by infer_relative_search_space().

Returns:

A dictionary containing the parameter names and the values.

Return type:

dict[str, Any]