optuna.samplers.nsgaii.UNDXCrossover

class optuna.samplers.nsgaii.UNDXCrossover(sigma_xi=0.5, sigma_eta=None)

Unimodal Normal Distribution Crossover used by NSGAIISampler.

Generates child individuals from the three parents using a multivariate normal distribution.

• H. Kita, I. Ono and S. Kobayashi, Multi-parental extension of the unimodal normal distribution crossover for real-coded genetic algorithms, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 1999, pp. 1581-1588 Vol. 2

Parameters

• sigma_xi (float) – Parametrizes normal distribution from which xi is drawn.

• sigma_eta (Optional[float]) – Parametrizes normal distribution from which etas are drawn. If not specified, defaults to 0.35 / sqrt(len(search_space)).

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