paulin/EasyGA
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Added comments & fixed small bug

Browse Source 
Mostly added comments, but also fixed a small bug in parent selection where the tournament size would be much smaller than it should be.
This commit is contained in:
RyleyGG
2020-10-12 09:23:41 -04:00
parent c3d9ef8bd1
commit 94d7c52666
7 changed files with 50 additions and 46 deletions
Download Patch File Download Diff File Expand all files Collapse all files

62
src/survivor_selection/survivor_selection_methods.py
View File

@ -5,32 +5,38 @@ from initialization.population_structure.population import Population
from initialization.chromosome_structure.chromosome import Chromosome
class Survivor_Selection:
def repeated_crossover(ga, next_population): #Might be cheating? I don't know honestly - RG
while len(next_population.get_all_chromosomes()) < ga.population_size:
crossover_pool = ga.population.mating_pool
"""Survivor selection determines which individuals should be brought to the next generation"""
split_point = random.randint(0,ga.chromosome_length)
chromosome_list = []
for i in range(len(crossover_pool)):
if i + 1 < len(crossover_pool):
new_gene_set = []
parent_one = crossover_pool[i].get_genes()
parent_two = crossover_pool[i+1].get_genes()
new_gene_set.extend(parent_one[0:split_point])
new_gene_set.extend(parent_two[split_point:])
new_chromosome = create_chromosome(new_gene_set)
chromosome_list.append(new_chromosome)
for i in range(len(chromosome_list)):
next_population.add_chromosome(chromosome_list[i])
if len(next_population.get_all_chromosomes()) >= ga.population_size:
break
return next_population
def remove_two_worst(ga, next_population):
iterator = 0
while len(next_population.get_all_chromosomes()) < ga.population_size:
next_population.add_chromosome(ga.population.get_all_chromosomes()[iterator])
iterator += 1
return next_population
""" Pretty sure this isn't actually survivor selection - seems like its 'cheating'
def repeated_crossover(ga, next_population):
while len(next_population.get_all_chromosomes()) < ga.population_size:
crossover_pool = ga.population.mating_pool
split_point = random.randint(0,ga.chromosome_length)
chromosome_list = []
for i in range(len(crossover_pool)):
if i + 1 < len(crossover_pool):
new_gene_set = []
parent_one = crossover_pool[i].get_genes()
parent_two = crossover_pool[i+1].get_genes()
new_gene_set.extend(parent_one[0:split_point])
new_gene_set.extend(parent_two[split_point:])
new_chromosome = create_chromosome(new_gene_set)
chromosome_list.append(new_chromosome)
for i in range(len(chromosome_list)):
next_population.add_chromosome(chromosome_list[i])
if len(next_population.get_all_chromosomes()) >= ga.population_size:
break
return next_population
"""
"""Will bring all but the worst-performing chromosomes from the current generation"""
"""The exact number of chromosomes removed depends on how many offspring were generated by parent selection"""
def remove_worst(ga, next_population):
iterator = 0
while len(next_population.get_all_chromosomes()) < ga.population_size:
next_population.add_chromosome(ga.population.get_all_chromosomes()[iterator])
iterator += 1
return next_population