from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
from optuna._experimental import experimental_class
from optuna.samplers.nsgaii._crossovers._base import BaseCrossover
if TYPE_CHECKING:
from optuna.study import Study
[docs]
@experimental_class("3.0.0")
class BLXAlphaCrossover(BaseCrossover):
"""Blend Crossover operation used by :class:`~optuna.samplers.NSGAIISampler`.
Uniformly samples child individuals from the hyper-rectangles created
by the two parent individuals. For further information about BLX-alpha crossover,
please refer to the following paper:
- `Eshelman, L. and J. D. Schaffer.
Real-Coded Genetic Algorithms and Interval-Schemata. FOGA (1992).
<https://doi.org/10.1016/B978-0-08-094832-4.50018-0>`_
Args:
alpha:
Parametrizes blend operation.
"""
n_parents = 2
def __init__(self, alpha: float = 0.5) -> None:
self._alpha = alpha
[docs]
def crossover(
self,
parents_params: np.ndarray,
rng: np.random.RandomState,
study: Study,
search_space_bounds: np.ndarray,
) -> np.ndarray:
# https://doi.org/10.1109/CEC.2001.934452
# Section 2 Crossover Operators for RCGA 2.1 Blend Crossover
parents_min = parents_params.min(axis=0)
parents_max = parents_params.max(axis=0)
diff = self._alpha * (parents_max - parents_min) # Equation (1).
low = parents_min - diff # Equation (1).
high = parents_max + diff # Equation (1).
r = rng.rand(len(search_space_bounds))
child_params = (high - low) * r + low
return child_params