optuna.samplers.nsgaii.SPXCrossover

class optuna.samplers.nsgaii.SPXCrossover(epsilon=None)[source]

Simplex Crossover operation used by NSGAIISampler.

Uniformly samples child individuals from within a single simplex that is similar to the simplex produced by the parent individual. For further information about SPX crossover, please refer to the following paper:

Shigeyoshi Tsutsui and Shigeyoshi Tsutsui and David E. Goldberg and David E. Goldberg and Kumara Sastry and Kumara Sastry Progress Toward Linkage Learning in Real-Coded GAs with Simplex Crossover. IlliGAL Report. 2000.

Parameters:: epsilon (float | None) – Expansion rate. If not specified, defaults to sqrt(len(search_space) + 2).

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