def reproduce(population):
"""Applies the evolutionary survival process to the population.
A few select elites with the best reward (fitness) is automatically part of the next generation.
From the population parents are selected to create offspring. The offspring will be part of
the next generation. The parents will cease to exist unless they were part of the elite.
Mutation is applied to the offspring, which has a chance of altering the chromosomes.
Args:
The population which is a list of the object Individual. The entire population just ran in
the simulation and their reward (fitness) was stored.
Returns:
A new population which is a list of the object Individual. The new population is the next generation
and consists of the elite from the previous population and offspring from the previous generation.
"""
# Sort the population by its reward (fitness)
population = sorted(population, key=lambda x: x.reward, reverse=True)
total_remove = 94
if judgement_day(population):
new_population = [population[0]]
for i in range(100 - len(new_population)):
new_population.append(random_neural_network())
return new_population
# Elitism
new_population = population[:(len(population) - total_remove)]
# Selection, may be replaced by tournament_selection method
parent_1, parent_2 = wheel_selection(total_remove, population)
for i in range(int(total_remove / 2)):
weight_1, weight_2 = uniform_crossover(parent_1[i].chromosome_1,
parent_2[i].chromosome_1)
bias_1, bias_2 = uniform_crossover(parent_1[i].chromosome_2,
parent_2[i].chromosome_2)
mutate(weight_1)
mutate(weight_2)
mutate(bias_1)
mutate(bias_2)
c1 = Individual()
c2 = Individual()
c1.chromosome_1 = weight_1
c1.chromosome_2 = bias_1
c2.chromosome_1 = weight_2
c2.chromosome_2 = bias_2
new_population.append(c1)
new_population.append(c2)
return new_population
def reproduce(population):
"""Applies the evolutionary survival process to the population.
A few select elites with the best reward (fitness) is automatically part of the next generation.
From the population parents are selected to create offspring. The offspring will be part of
the next generation. The parents will cease to exist unless they were part of the elite.
Mutation is applied to the offspring, which has a chance of altering the chromosomes.
Args:
The population which is a list of the object Individual. The entire population just ran in
the simulation and their reward (fitness) was stored.
Returns:
A new population which is a list of the object Individual. The new population is the next generation
and consists of the elite from the previous population and offspring from the previous generation.
"""
# Sort the population to find out which had the best performance
population = sorted(population, key=lambda x: x.reward, reverse=True)
total_remove = 94
# Elitism
new_population = population[:(len(population) - total_remove)]
parent_1, parent_2 = wheel_selection(total_remove, population)
for i in range(int(total_remove / 2)):
if random_offspring(parent_1[i], parent_2[i]):
individual_1, individual_2 = create_random_offspring()
else:
rule_1, rule_2 = uniform_crossover_binary(parent_1[i].chromosome_1,
parent_2[i].chromosome_1)
range_1, range_2 = uniform_crossover(parent_1[i].chromosome_2,
parent_2[i].chromosome_2)
mutate(rule_1)
mutate(rule_2)
ca_mutate(range_1)
ca_mutate(range_2)
individual_1 = Individual()
individual_2 = Individual()
individual_1.chromosome_1 = rule_1
individual_2.chromosome_1 = rule_2
individual_1.chromosome_2 = range_1
individual_2.chromosome_2 = range_2
new_population.append(individual_1)
new_population.append(individual_2)
return new_population
