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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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from math import floor, ceil |
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import numpy as np |
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import random |
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from ...base_optimizer import BaseOptimizer |
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from ..local import HillClimbingPositioner |
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class EvolutionStrategyOptimizer(BaseOptimizer): |
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def __init__(self, _opt_args_): |
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super().__init__(_opt_args_) |
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self.n_positioners = self._opt_args_.individuals |
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self.n_mutations = floor(self.n_positioners * self._opt_args_.mutation_rate) |
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self.n_crossovers = ceil(self.n_positioners * self._opt_args_.crossover_rate) |
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def _init_individual(self, _cand_): |
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_p_ = Individual(**self._opt_args_.kwargs_opt) |
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_p_.move_random(_cand_) |
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return _p_ |
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def _mutate_individuals(self, _cand_, mutate_idx): |
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p_list_mutate = [self.p_list[i] for i in mutate_idx] |
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for _p_ in p_list_mutate: |
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_p_.move_climb(_cand_, _p_.pos_new) |
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def _crossover(self, _cand_, cross_idx, replace_idx): |
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p_list_replace = [self.p_list[i] for i in replace_idx] |
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for i, _p_ in enumerate(p_list_replace): |
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j = i + 1 |
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if j == len(cross_idx): |
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j = 0 |
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pos_new = self._cross_two_ind( |
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[ |
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[self.p_list[i] for i in cross_idx][i], |
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[self.p_list[i] for i in cross_idx][j], |
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] |
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) |
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_p_.pos_new = pos_new |
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def _cross_two_ind(self, p_list): |
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pos_new = [] |
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for pos1, pos2 in zip(p_list[0].pos_new, p_list[1].pos_new): |
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rand = random.randint(0, 1) |
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if rand == 0: |
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pos_new.append(pos1) |
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else: |
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pos_new.append(pos2) |
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return np.array(pos_new) |
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def _move_positioners(self, _cand_): |
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idx_sorted_ind = self._rank_individuals() |
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mutate_idx, cross_idx, replace_idx = self._select_individuals(idx_sorted_ind) |
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self._crossover(_cand_, cross_idx, replace_idx) |
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self._mutate_individuals(_cand_, mutate_idx) |
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def _rank_individuals(self): |
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scores_list = [] |
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for _p_ in self.p_list: |
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scores_list.append(_p_.score_current) |
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scores_np = np.array(scores_list) |
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idx_sorted_ind = list(scores_np.argsort()[::-1]) |
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return idx_sorted_ind |
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def _select_individuals(self, index_best): |
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mutate_idx = index_best[: self.n_mutations] |
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cross_idx = index_best[: self.n_crossovers] |
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n = self.n_crossovers |
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replace_idx = index_best[-n:] |
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return mutate_idx, cross_idx, replace_idx |
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def _iterate(self, i, _cand_): |
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_p_current = self.p_list[i % self.n_positioners] |
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self._move_positioners(_cand_) |
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self._optimizer_eval(_cand_, _p_current) |
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self._update_pos(_cand_, _p_current) |
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return _cand_ |
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def _init_iteration(self, _cand_): |
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p = self._init_individual(_cand_) |
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self._optimizer_eval(_cand_, p) |
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self._update_pos(_cand_, p) |
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return p |
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class Individual(HillClimbingPositioner): |
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def __init__(self, *args, **kwargs): |
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super().__init__(*args, **kwargs) |
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SimonBlanke /
Hyperactive
