optuna.samplers.nsgaii.SBXCrossover

class optuna.samplers.nsgaii.SBXCrossover(eta=None, uniform_crossover_prob=0.5, use_child_gene_prob=0.5)[source]

Simulated Binary Crossover operation used by NSGAIISampler.

Generates a child from two parent individuals according to the polynomial probability distribution.

In the paper, SBX has only one argument, eta, and generate two child individuals. However, Optuna can only return one child individual in one crossover operation, so it uses the uniform_crossover_prob and use_child_gene_prob arguments to make two individuals into one.

Parameters:

  • eta (float | None) – Distribution index. A small value of eta allows distant solutions to be selected as children solutions. If not specified, takes default value of 2 for single objective functions and 20 for multi objective.

  • uniform_crossover_prob (float) – uniform_crossover_prob is the probability of uniform crossover between two individuals selected as candidate child individuals. This argument is whether or not two individuals are crossover to make one child individual. If the uniform_crossover_prob exceeds 0.5, the result is equivalent to 1-uniform_crossover_prob, because it returns one of the two individuals of the crossover result. If not specified, takes default value of 0.5. The range of values is [0.0, 1.0].

  • use_child_gene_prob (float) – use_child_gene_prob is the probability of using the value of the generated child variable rather than the value of the parent. This probability is applied to each variable individually. where 1-use_chile_gene_prob is the probability of using the parent’s values as it is. If not specified, takes default value of 0.5. The range of values is (0.0, 1.0].

Note

Added in v3.0.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.0.0.

Methods

crossover(parents_params, rng, study, ...)

Perform crossover of selected parent individuals.

Attributes

n_parents

crossover(parents_params, rng, study, search_space_bounds)[source]

Perform crossover of selected parent individuals.

This method is called in sample_relative().

Parameters:

  • parents_params (np.ndarray) – A numpy.ndarray with dimensions num_parents x num_parameters. Represents a parameter space for each parent individual. This space is continuous for numerical parameters.

  • rng (np.random.RandomState) – An instance of numpy.random.RandomState.

  • study (Study) – Target study object.

  • search_space_bounds (np.ndarray) – A numpy.ndarray with dimensions len_search_space x 2 representing numerical distribution bounds constructed from transformed search space.

Returns:

A 1-dimensional numpy.ndarray containing new parameter combination.

Return type:

np.ndarray