def newGeneration():
global reproduction_count
reproduction_count = 0
global next_generation
# if len(next_generation) > 1:
# if len(next_generation) < 10:
# neat.species_threshold -= 0.3
# elif len(next_generation) >= 10:
# neat.species_threshold +=0.3
children = []
calculateAverageFitness(next_generation)
updateStagnationInformation()
removeStagnantSpecies()
calculateAverageFitness(next_generation)
totalFitness = totalAverageFitness(next_generation)
for x in next_generation:
genelog.info("[Species : " + str(x.number) + " Organisms: " +
str(len(x.organisms)) + " ]")
x.sort()
if (len(x.organisms) > 10):
x.removeUnfit()
genelog.info("[Trim Step- Organisms Survived: " +
str(len(x.organisms)) + " ]")
breedCount = int((x.repFitness / totalFitness) * gen_iterations) - 1
for i in range(breedCount):
if random.random() < crossover_rate and len(x.organisms) > 1:
genelog.info("[ORGANISM]")
xx = random.randrange(0, len(x.organisms))
xy = random.randrange(0, len(x.organisms))
while xx == xy:
xx = random.randrange(0, len(x.organisms))
xy = random.randrange(0, len(x.organisms))
if x.organisms[xy].global_fitness > x.organisms[
xx].global_fitness:
temp = xx
xx = xy
xy = temp
childGenome = Genome.crossover(x.organisms[xx].genome,
x.organisms[xy].genome)
reproduction_count += 1
# apply random chance of further mutation
childGenome.mutate()
childGenome.mutate_topology()
childOrganism = Agent(i_shape, o_shape, childGenome,
agent_name)
# TODO: random enable disable genes
children.append(childOrganism)
else:
xx = random.randrange(0, len(x.organisms))
childGenome = copy.deepcopy(x.organisms[xx].genome)
childGenome.mutate()
childOrganism = Agent(i_shape, o_shape, childGenome,
agent_name)
children.append(childOrganism)
reproduction_count += 1
if random.random() < mating_rate and len(next_generation) > 1:
print("Interspecies Breeding")
xx = x.organisms[0]
xy = next_generation[random.randrange(
0, len(next_generation))].organisms[0]
while xx == xy:
xy = next_generation[random.randrange(
0, len(next_generation))].organisms[0]
childGenome = Genome.crossover(xx.genome, xy.genome)
reproduction_count += 1
childGenome.mutate()
childGenome.mutate_topology()
childOrganism = Agent(i_shape, o_shape, childGenome, agent_name)
children.append(childOrganism)
for species in next_generation:
species.reduce()
for organism in children:
speciate(organism)
global exceeded
def test(genome, node_inno, con_inno):
genome[2] = Genome.crossover(genome[0], genome[1], 0.10)
print(genome[2])
pass
