optuna.samplers.nsgaii.VSBXCrossover

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

Modified Simulated Binary Crossover operation used by NSGAIISampler.

vSBX generates child individuals without excluding any region of the parameter space, while maintaining the excellent properties of SBX.

• Pedro J. Ballester, Jonathan N. Carter. Real-Parameter Genetic Algorithms for Finding Multiple Optimal Solutions in Multi-modal Optimization. GECCO 2003: 706-717

Parameters:

eta (Optional[float]) – 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 (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