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 (int) – Number of individuals (trials) in a generation.
mutation_prob (Optional[float]) – Probability of mutating each parameter when creating a new individual. If
None
is specified, the value1.0 / len(parent_trial.params)
is used whereparent_trial
is the parent trial of the target individual.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.
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 usingsample_independent()
method.- Parameters
study (MultiObjectiveStudy) – Target study object.
trial (FrozenMultiObjectiveTrial) – Target trial object.
- Returns
A dictionary containing the parameter names and parameter’s distributions.
- Return type
See also
Please refer to
intersection_search_space()
as an implementation ofinfer_relative_search_space()
.
- 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 optionn_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
study (MultiObjectiveStudy) – Target study object.
trial (FrozenMultiObjectiveTrial) – Target trial 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
- 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
study (MultiObjectiveStudy) – Target study object.
trial (FrozenMultiObjectiveTrial) – Target trial 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