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Added iterable features

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SimpleArt
2020-11-19 11:46:47 -05:00
parent 6e95ff5d9d
commit 0ee545429c
7 changed files with 167 additions and 97 deletions
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18
src/parent_selection/parent_selection_methods.py
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@ -21,15 +21,15 @@ class Parent_Selection:
# Choose the tournament size.
# Use no less than 5 chromosomes per tournament.
tournament_size = int(ga.population.size()*ga.tournament_size_ratio)
tournament_size = int(len(ga.population)*ga.tournament_size_ratio)
if tournament_size < 5:
tournament_size = 5
# Repeat tournaments until the mating pool is large enough.
while (len(ga.population.get_mating_pool()) < ga.population.size()*ga.parent_ratio):
while (len(ga.population.get_mating_pool()) < len(ga.population)*ga.parent_ratio):
# Generate a random tournament group and sort by fitness.
tournament_group = sorted([random.randint(0, ga.population.size()-1) for n in range(tournament_size)])
tournament_group = sorted([random.randint(0, len(ga.population)-1) for n in range(tournament_size)])
# For each chromosome, add it to the mating pool based on its rank in the tournament.
for index in range(tournament_size):
@ -43,7 +43,7 @@ class Parent_Selection:
ga.population.set_parent(tournament_group[index])
# Stop if parent ratio reached
if len(ga.population.get_mating_pool()) >= ga.population.size()*ga.parent_ratio:
if len(ga.population.get_mating_pool()) >= len(ga.population)*ga.parent_ratio:
break
@ -73,19 +73,19 @@ class Parent_Selection:
return
# The sum of all the fitnessess in a population
fitness_sum = sum(ga.get_chromosome_fitness(index) for index in range(ga.population.size()))
fitness_sum = sum(ga.get_chromosome_fitness(index) for index in range(len(ga.population)))
# A list of ranges that represent the probability of a chromosome getting chosen
probability = [ga.selection_probability]
# The chance of being selected increases incrementally
for index in range(ga.population.size()):
for index in range(len(ga.population)):
probability.append(probability[-1]+ga.get_chromosome_fitness(index)/fitness_sum)
probability = probability[1:]
# Loops until it reaches a desired mating pool size
while (len(ga.population.get_mating_pool()) < ga.population.size()*ga.parent_ratio):
while (len(ga.population.get_mating_pool()) < len(ga.population)*ga.parent_ratio):
# Spin the roulette
rand_number = random.random()
@ -121,10 +121,10 @@ class Parent_Selection:
return
# Loops until it reaches a desired mating pool size
while (len(ga.population.get_mating_pool()) < ga.population.size()*ga.parent_ratio):
while (len(ga.population.get_mating_pool()) < len(ga.population)*ga.parent_ratio):
# Selected chromosome
index = random.randint(0, ga.population.size()-1)
index = random.randint(0, len(ga.population)-1)
# Probability of becoming a parent is fitness/max_fitness
if random.uniform(ga.selection_probability, 1) < ga.get_chromosome_fitness(index)/max_fitness: