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Fix uniform crossover function

This commit is contained in:
coolneng 2021-06-20 18:04:23 +02:00
parent 046b1a043a
commit 5f89731f77
Signed by: coolneng
GPG Key ID: 9893DA236405AF57
1 changed files with 22 additions and 10 deletions
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32
src/genetic_algorithm.py
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@ -5,6 +5,7 @@ from math import ceil
from functools import partial
from multiprocessing import Pool
from copy import deepcopy
from itertools import chain, repeat
from preprocessing import parse_file
@ -130,12 +131,15 @@ def position_crossover(parents, m):
def crossover(mode, parents, m):
split_parents = zip(*[iter(parents)] * 2)
split_parents = list(zip(*[iter(parents)] * 2))
offspring = []
if mode == "uniform":
crossover_func = partial(uniform_crossover, m=m)
for i in range(len(split_parents)):
offspring.append(uniform_crossover(split_parents[i], m))
offspring.append(uniform_crossover(split_parents[i], m))
else:
crossover_func = partial(position_crossover, m=m)
offspring = [*map(crossover_func, split_parents)]
offspring = [*map(crossover_func, split_parents)]
return offspring
@ -211,18 +215,26 @@ def get_best_elements(population):
def get_worst_elements(population):
first_index = population.index(min(population, key=lambda x: all(x.fitness)))
population.pop(first_index)
second_index = population.index(min(population, key=lambda x: all(x.fitness)))
select_population = deepcopy(population)
first_element = min(select_population, key=lambda x: x.fitness.values[0])
first_index = get_individual_index(first_element, select_population)
select_population.pop(first_index)
second_element = min(select_population, key=lambda x: x.fitness.values[0])
second_index = get_individual_index(second_element, select_population)
return first_index, second_index
def stationary_replacement(prev_population, current_population):
new_population = prev_population
worst_indexes = get_worst_elements(prev_population)
best_indexes = get_best_elements(current_population)
first_worst, second_worst = get_worst_elements(prev_population)
first_best, second_best = get_best_elements(current_population)
worst_indexes = [first_worst, second_worst]
best_indexes = [first_best, second_best]
for worst, best in zip(worst_indexes, best_indexes):
if current_population[best].fitness > prev_population[worst].fitness:
if (
current_population[best].fitness.values[0]
> prev_population[worst].fitness.values[0]
):
new_population[worst] = current_population[best]
return new_population
@ -276,5 +288,5 @@ genetic_algorithm(
data=data,
select_mode="generational",
crossover_mode="uniform",
max_iterations=1,
max_iterations=10,
)