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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from .simulated_annealing import SimulatedAnnealingOptimizer
from ...search import Search
class StochasticTunnelingOptimizer(SimulatedAnnealingOptimizer, Search):
def __init__(self, search_space, gamma=0.5, **kwargs):
super().__init__(search_space, **kwargs)
def _accept(self):
f_stun = 1 - np.exp(-self._opt_args_.gamma * self._score_norm())
return np.exp(-f_stun / self.temp)
SimonBlanke /
Gradient-Free-Optimizers
