optuna.samplers.nsgaii.SBXCrossover
- class optuna.samplers.nsgaii.SBXCrossover(eta=None)[source]
Simulated Binary Crossover operation used by
NSGAIISampler
.Generates a child from two parent individuals according to the polynomial probability distribution.
- 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 of2
for single objective functions and20
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)[source]
Perform crossover of selected parent individuals.
This method is called in
sample_relative()
.- Parameters:
parents_params (np.ndarray) – A
numpy.ndarray
with dimensionsnum_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 dimensionslen_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