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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from ..local import StochasticHillClimbingOptimizer
from ...search import Search
class SimulatedAnnealingOptimizer(StochasticHillClimbingOptimizer, Search):
def __init__(self, search_space, annealing_rate=0.99, start_temp=100):
super().__init__(search_space)
self.annealing_rate = annealing_rate
self.temp = start_temp
# use _consider from StochasticHillClimbingOptimizer
def _accept_default(self):
return np.exp(-self._score_norm_default() / self.temp)
def _accept_adapt(self):
return self._score_norm_adapt() * self.temp
def evaluate(self, score_new):
super().evaluate(score_new)
self.temp = self.temp * self.annealing_rate
SimonBlanke /
Gradient-Free-Optimizers
