def run(self):
for generation in range(1, self.Config.NUMBER_OF_GENERATIONS):
# Get Fitness of Every Genome
for genome in self.population:
genome.fitness = max(0, self.Config.fitness_fn(genome))
best_genome = utils.get_best_genome(self.population)
# Reproduce
all_fitnesses = []
remaining_species = []
for species, is_stagnant in Species.stagnation(
self.species, generation):
if is_stagnant:
self.species.remove(species)
else:
all_fitnesses.extend(g.fitness for g in species.members)
remaining_species.append(species)
min_fitness = min(all_fitnesses)
max_fitness = max(all_fitnesses)
fit_range = max(1.0, (max_fitness - min_fitness))
for species in remaining_species:
# Set adjusted fitness
avg_species_fitness = np.mean(
[g.fitness for g in species.members])
species.adjusted_fitness = (avg_species_fitness -
min_fitness) / fit_range
adj_fitnesses = [s.adjusted_fitness for s in remaining_species]
adj_fitness_sum = sum(adj_fitnesses)
# Get the number of offspring for each species
new_population = []
for species in remaining_species:
if species.adjusted_fitness > 0:
size = max(
2,
int((species.adjusted_fitness / adj_fitness_sum) *
self.Config.POPULATION_SIZE))
else:
size = 2
# sort current members in order of descending fitness
cur_members = species.members
cur_members.sort(key=lambda g: g.fitness, reverse=True)
species.members = [] # reset
# save top individual in species
new_population.append(cur_members[0])
size -= 1
# Only allow top x% to reproduce
purge_index = int(self.Config.PERCENTAGE_TO_SAVE *
len(cur_members))
purge_index = max(2, purge_index)
cur_members = cur_members[:purge_index]
for i in range(size):
parent_1 = random.choice(cur_members)
parent_2 = random.choice(cur_members)
child = crossover(parent_1, parent_2, self.Config)
mutate(child, self.Config)
new_population.append(child)
# Set new population
self.population = new_population
Population.current_gen_innovation = []
# Speciate
for genome in self.population:
self.speciate(genome, generation)
if best_genome.fitness >= self.Config.FITNESS_THRESHOLD:
return best_genome, generation
# Generation Stats
if self.Config.VERBOSE:
logger.info(f'Finished Generation {generation}')
logger.info(f'Best Genome Fitness: {best_genome.fitness}')
logger.info(
f'Best Genome Length {len(best_genome.connection_genes)}\n'
)
return None, None
def run(self, pool=None, shared_data=None):
allgenfitnesses = []
for generation in range(1, self.Config.NUMBER_OF_GENERATIONS):
# ****** BYME: Neuro-evolution accures here *******
# Get Fitness of Every Genome
if pool != None:
ll = len(self.population)
args = zip(list(it.repeat(self.Config.fitness_fn, ll)), \
self.population, list(it.repeat(shared_data, ll)), \
list(it.repeat(generation, ll)), range(ll))
fitnesses = list(pool.map(pool_func, args))
for genome, fitness in zip(self.population, fitnesses):
genome.fitness = fitness
else:
for genome in tqdm(self.population):
genome.fitness = max(0, self.Config.fitness_fn(genome))
allfitnesses_onegen = [g.fitness for g in self.population]
allfitnesses_onegen.sort()
allgenfitnesses.append(allfitnesses_onegen)
best_genome = utils.get_best_genome(self.population)
draw_net(best_genome, view=False, \
filename="./images/solution-best-g%d"%(generation), show_disabled=True)
# Reproduce
all_fitnesses = []
remaining_species = []
for species, is_stagnant in Species.stagnation(
self.species, generation):
if is_stagnant:
self.species.remove(species)
else:
all_fitnesses.extend(g.fitness for g in species.members)
remaining_species.append(species)
min_fitness = min(all_fitnesses)
max_fitness = max(all_fitnesses)
fit_range = max(1.0, (max_fitness - min_fitness))
for species in remaining_species:
# Set adjusted fitness
avg_species_fitness = np.mean(
[g.fitness for g in species.members])
species.adjusted_fitness = (avg_species_fitness -
min_fitness) / fit_range
adj_fitnesses = [s.adjusted_fitness for s in remaining_species]
adj_fitness_sum = sum(adj_fitnesses)
# Get the number of offspring for each species
new_population = []
for species in remaining_species:
if species.adjusted_fitness > 0:
size = max(
2,
int((species.adjusted_fitness / adj_fitness_sum) *
self.Config.POPULATION_SIZE))
else:
size = 2
# sort current members in order of descending fitness
cur_members = species.members
cur_members.sort(key=lambda g: g.fitness, reverse=True)
species.members = [] # reset
# save top individual in species
new_population.append(cur_members[0])
size -= 1
# Only allow top x% to reproduce
purge_index = int(self.Config.PERCENTAGE_TO_SAVE *
len(cur_members))
purge_index = max(2, purge_index)
cur_members = cur_members[:purge_index]
for i in range(size):
parent_1 = random.choice(cur_members)
parent_2 = random.choice(cur_members)
child = crossover(parent_1, parent_2, self.Config)
mutate(child, self.Config)
new_population.append(child)
# Set new population
self.population = new_population
Population.current_gen_innovation = []
# Speciate
for genome in self.population:
self.speciate(genome, generation)
if best_genome.fitness >= self.Config.FITNESS_THRESHOLD:
o = open("allgenfitnesses.txt", "w")
for allfitnesses_onegen in allgenfitnesses:
o.write(str(allfitnesses_onegen) + "\n")
o.close()
return best_genome, generation
# Generation Stats
if self.Config.VERBOSE:
print('Finished Generation', generation)
print('Best Genome Fitness:', best_genome.fitness)
if hasattr(best_genome, "avgloss"):
print('Best Genome Loss:', best_genome.avgloss)
print('Best Genome Length', len(best_genome.connection_genes))
print()
o = open("allgenfitnesses.txt", "w")
for allfitnesses_onegen in allgenfitnesses:
o.write(str(allfitnesses_onegen) + "\n")
o.close()
return None, None