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Implement position crossover operator

This commit is contained in:
coolneng 2021-05-25 16:53:59 +02:00
parent a80b8842e3
commit f2584f87cb
Signed by: coolneng
GPG Key ID: 9893DA236405AF57
1 changed files with 33 additions and 13 deletions
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46
src/genetic_algorithm.py
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@ -1,5 +1,5 @@
from numpy import sum, append, arange, delete, where from numpy import sum, append, arange, delete, where
from numpy.random import randint, choice from numpy.random import randint, choice, shuffle
from pandas import DataFrame from pandas import DataFrame
@ -44,14 +44,22 @@ def evaluate_element(element, data):
return sum(fitness) return sum(fitness)
def select_random_genes(matching_genes, parents, m): def select_distinct_genes(matching_genes, parents, m):
cutoff = randint(m) cutoff = randint(m)
distinct_genes = delete(arange(m), matching_genes) distinct_indexes = delete(arange(m), matching_genes)
first_parent_genes = parents[0].point.iloc[distinct_genes[cutoff:]] first_parent_genes = parents[0].point.iloc[distinct_indexes[cutoff:]]
second_parent_genes = parents[1].point.iloc[distinct_genes[:cutoff]] second_parent_genes = parents[1].point.iloc[distinct_indexes[:cutoff]]
return first_parent_genes, second_parent_genes return first_parent_genes, second_parent_genes
def select_random_genes(matching_genes, parents, m):
random_parent = parents[randint(len(parents))]
distinct_indexes = delete(arange(m), matching_genes)
genes = random_parent.point.iloc[distinct_indexes].values
shuffle(genes)
return genes
def repair_offspring(offspring, parents, m): def repair_offspring(offspring, parents, m):
while len(offspring) != m: while len(offspring) != m:
if len(offspring) > m: if len(offspring) > m:
@ -64,6 +72,7 @@ def repair_offspring(offspring, parents, m):
offspring = offspring.append( offspring = offspring.append(
{"point": best_point, "distance": 0}, ignore_index=True {"point": best_point, "distance": 0}, ignore_index=True
) )
random_parent.drop(index=best_index, inplace=True)
return offspring return offspring
@ -73,20 +82,31 @@ def get_matching_genes(parents):
return where(first_parent == second_parent) return where(first_parent == second_parent)
def uniform_crossover(parents, m): def populate_offspring(values):
offspring = DataFrame(columns=["point", "distance"]) offspring = DataFrame(columns=["point", "distance"])
matching_genes = get_matching_genes(parents) for element in values:
offspring["point"] = parents[0].point.iloc[matching_genes] aux = DataFrame(columns=["point", "distance"])
first_genes, second_genes = select_random_genes(matching_genes, parents, m) aux["point"] = element
offspring["point"] = offspring["point"].append(first_genes) offspring = offspring.append(aux)
offspring["point"] = offspring["point"].append(second_genes)
offspring["distance"] = 0 offspring["distance"] = 0
offspring = offspring[1:]
return offspring
def uniform_crossover(parents, m):
matching_indexes = get_matching_genes(parents)
matching_genes = parents[0].point.iloc[matching_indexes]
first_genes, second_genes = select_distinct_genes(matching_genes, parents, m)
offspring = populate_offspring(values=[matching_genes, first_genes, second_genes])
viable_offspring = repair_offspring(offspring, parents, m) viable_offspring = repair_offspring(offspring, parents, m)
return viable_offspring return viable_offspring
def position_crossover(parents, n): def position_crossover(parents, m):
genotypes = [parents[0].point.values, parents[1].point.values] matching_genes = get_matching_genes(parents)
shuffled_genes = select_random_genes(matching_genes, parents, m)
offspring = populate_offspring(values=[matching_genes, shuffled_genes])
return offspring
def crossover(mode, parents, m): def crossover(mode, parents, m):