for testing and deploying your application
for finding and fixing issues
for empowering human code reviews
# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
from . import HillClimbingOptimizer
from ...search import Search
class RepulsingHillClimbingOptimizer(HillClimbingOptimizer, Search):
def __init__(
self,
search_space,
epsilon=0.03,
distribution="normal",
n_neighbours=3,
repulsion_factor=5,
rand_rest_p=0.03,
):
super().__init__(search_space, epsilon, distribution, n_neighbours)
self.tabus = []
self.repulsion_factor = repulsion_factor
self.rand_rest_p = rand_rest_p
self.epsilon_mod = 1
@HillClimbingOptimizer.track_nth_iter
@HillClimbingOptimizer.random_restart
def iterate(self):
return self._move_climb(self.pos_current, epsilon_mod=self.epsilon_mod)
def evaluate(self, score_new):
super().evaluate(score_new)
if score_new <= self.score_current:
self.epsilon_mod = self.repulsion_factor
else:
SimonBlanke /
Gradient-Free-Optimizers
