optuna.samplers.nsgaii.SBXCrossover

class optuna.samplers.nsgaii.SBXCrossover(eta=None)

Simulated Binary Crossover operation used by NSGAIISampler.

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

• Deb, K. and R. Agrawal. “Simulated Binary Crossover for Continuous Search Space.” Complex Syst. 9 (1995): n. pag.

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.

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)

Perform crossover of selected parent individuals.

This method is called in sample_relative().

Parameters:

• parents_params (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 (RandomState) – An instance of numpy.random.RandomState.

• study (Study) – Target study object.

• search_space_bounds (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:

ndarray