def __init__(self, population):
logging.info("Starting Pool Initialization")
self.innovation = 0
self.population = population
self.species = []
self.generation = 0
self.fitness = []
genome = Genome(self.new_innovation)
genome.set_basic()
logging.info("Adding genomes to initial pool")
for i in range(population):
logging.info("Adding genome %d" % i)
self.add_to_species(genome.copy())
logging.info("Pool initialization complete")
def spawn(self, genome: Genome, size):
new_genome: genome
for count in range(0, size):
new_genome = genome.copy()
# mutate
new_genome.mutate_link_weights(power=Neat.weight_mut_power,
mode="COLDGAUSSPOINT")
# randomize traits
# TODO
# create new organism
# TODO
self.genomes.append(new_genome)
# keep record of current node id and innovation num
self.cur_innov_num = new_genome.get_last_gene_innovnum
self.cur_node_id = new_genome.get_last_node_id
# seperate new population into species
self.speciate()
return True
def reproduce_with_species(self, species_set, pop_size, generation):
'''
Creates and speciates genomes.
species_set -- set of current species
pop_size -- population size
generation -- current generation
'''
all_fitnesses = []
remaining_species = []
# Traverse species and grab fitnesses from non-stagnated species
for sid, species, species_is_stagnant in self.stagnation.update(
species_set, generation):
if species_is_stagnant:
print("!!! Species {} Stagnated !!!".format(sid))
# self.reporters.species_stagnant(sid, species)
pass
else:
# Add fitnesses of members of current species
all_fitnesses.extend(member.fitness
for member in itervalues(species.members))
remaining_species.append(species)
# No species
if not remaining_species:
species_set.species = {}
return {}
# Find min/max fitness across entire population
min_population_fitness = min(all_fitnesses)
max_population_fitness = max(all_fitnesses)
# Do not allow the fitness range to be zero, as we divide by it below.
population_fitness_range = max(
1.0, max_population_fitness - min_population_fitness)
# Compute adjusted fitness and record minimum species size
for species in remaining_species:
# Determine current species average fitness
mean_species_fitness = mean(
[member.fitness for member in itervalues(species.members)])
max_species_fitness = max(
[member.fitness for member in itervalues(species.members)])
# Determine current species adjusted fitness and update it
species_adjusted_fitness = (
mean_species_fitness -
min_population_fitness) / population_fitness_range
species.adjusted_fitness = species_adjusted_fitness
species.max_fitness = max_species_fitness
adjusted_fitnesses = [
species.adjusted_fitness for species in remaining_species
]
avg_adjusted_fitness = mean(adjusted_fitnesses)
# Compute the number of new members for each species in the new generation.
previous_sizes = [
len(species.members) for species in remaining_species
]
min_species_size = max(2, self.species_elitism)
spawn_amounts = self.compute_species_sizes(adjusted_fitnesses,
previous_sizes, pop_size,
min_species_size)
new_population = {}
species_set.species = {}
for spawn, species in zip(spawn_amounts, remaining_species):
# If elitism is enabled, each species always at least gets to retain its elites.
spawn = max(spawn, self.species_elitism)
assert spawn > 0
# The species has at least one member for the next generation, so retain it.
old_species_members = list(iteritems(species.members))
# Update species with blank slate
species.members = {}
# Update species in species set accordingly
species_set.species[species.key] = species
# Sort old species members in order of descending fitness.
old_species_members.sort(reverse=True, key=lambda x: x[1].fitness)
# Clone elites to new generation.
if self.species_elitism > 0:
for member_key, member in old_species_members[:self.
species_elitism]:
new_population[member_key] = member
spawn -= 1
# If the species only has room for the elites, move onto next species
if spawn <= 0: continue
# Only allow fraction of species members to reproduce
reproduction_cutoff = int(
ceil(self.species_reproduction_threshold *
len(old_species_members)))
# Use at least two parents no matter what the threshold fraction result is.
reproduction_cutoff = max(reproduction_cutoff, 2)
old_species_members = old_species_members[:reproduction_cutoff]
# Randomly choose parents and produce the number of offspring allotted to the species.
# NOTE: Asexual reproduction for now
while spawn > 0:
spawn -= 1
parent1_key, parent1 = random.choice(old_species_members)
# parent2_key, parent2 = random.choice(old_species_members)
child_key = next(self.genome_indexer)
child = Genome(child_key)
# child.crossover(parent1, parent2)
child.copy(parent1, generation)
child.mutate(generation)
new_population[child_key] = child
return new_population
