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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import random |
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import numpy as np |
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from ._evolutionary_algorithm import EvolutionaryAlgorithmOptimizer |
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from ._individual import Individual |
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class EvolutionStrategyOptimizer(EvolutionaryAlgorithmOptimizer): |
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name = "Evolution Strategy" |
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_name_ = "evolution_strategy" |
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__name__ = "EvolutionStrategyOptimizer" |
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optimizer_type = "population" |
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computationally_expensive = False |
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View Code Duplication |
def __init__( |
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self, |
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search_space, |
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initialize={"grid": 4, "random": 2, "vertices": 4}, |
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constraints=[], |
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random_state=None, |
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rand_rest_p=0, |
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nth_process=None, |
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population=10, |
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offspring=20, |
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replace_parents=False, |
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mutation_rate=0.7, |
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crossover_rate=0.3, |
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): |
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super().__init__( |
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search_space=search_space, |
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initialize=initialize, |
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constraints=constraints, |
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random_state=random_state, |
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rand_rest_p=rand_rest_p, |
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nth_process=nth_process, |
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) |
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self.population = population |
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self.offspring = offspring |
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self.replace_parents = replace_parents |
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self.mutation_rate = mutation_rate |
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self.crossover_rate = crossover_rate |
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self.individuals = self._create_population(Individual) |
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self.optimizers = self.individuals |
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def _cross(self): |
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while True: |
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if len(self.individuals) > 2: |
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rnd_int2 = random.choice( |
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[ |
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i |
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for i in range(0, self.n_ind - 1) |
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if i not in [self.rnd_int] |
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] |
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) |
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else: |
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rnd_int2 = random.choice( |
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[i for i in range(0, self.n_ind) if i not in [self.rnd_int]] |
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) |
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p_sec = self.pop_sorted[rnd_int2] |
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p_worst = self.pop_sorted[-1] |
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two_best_pos = [self.p_current.pos_current, p_sec.pos_current] |
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pos_new = self.discrete_recombination(two_best_pos) |
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self.p_current = p_worst |
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p_worst.pos_new = pos_new |
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if self.conv.not_in_constraint(pos_new): |
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return pos_new |
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return self.p_current.move_climb(pos_new) |
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@EvolutionaryAlgorithmOptimizer.track_new_pos |
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def init_pos(self): |
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nth_pop = self.nth_trial % len(self.individuals) |
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self.p_current = self.individuals[nth_pop] |
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return self.p_current.init_pos() |
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View Code Duplication |
@EvolutionaryAlgorithmOptimizer.track_new_pos |
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def iterate(self): |
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self.n_ind = len(self.individuals) |
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if self.n_ind == 1: |
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self.p_current = self.individuals[0] |
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return self.p_current.iterate() |
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self.sort_pop_best_score() |
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self.rnd_int = random.randint(0, len(self.pop_sorted) - 1) |
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self.p_current = self.pop_sorted[self.rnd_int] |
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total_rate = self.mutation_rate + self.crossover_rate |
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rand = np.random.uniform(low=0, high=total_rate) |
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if rand <= self.mutation_rate: |
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return self.p_current.iterate() |
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else: |
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return self._cross() |
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@EvolutionaryAlgorithmOptimizer.track_new_score |
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def evaluate(self, score_new): |
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self.p_current.evaluate(score_new) |
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SimonBlanke /
Gradient-Free-Optimizers
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