def mutateWeight(self):
rand = random.uniform(0, 1)
if rand < RANDOM_WEIGHT_PROB:
self.weight = random.uniform(-1, 1)
elif rand < PERTRUBE_WEIGHT_PROB:
self.weight += random.gauss(0, 1) * WEIGHT_MUTATION_POWER
self.weight = max(min(self.weight, WEIGHT_CAP), -WEIGHT_CAP)
def __init__(self, in_node, out_node, weight=None, disable_chance=0.0):
assert in_node.depth < out_node.depth, f'in {in_node} has depth {in_node.depth}, out {out_node} has depth {out_node.depth}'
self.innovation = self.getInnovation(in_node, out_node)
if self.innovation is None:
self.innovation = self.addInnovation(in_node, out_node)
self.in_ = in_node
self.out = out_node
self.weight = random.uniform(-1, 1) if weight is None else weight
self.enabled = random.uniform(0, 1) >= disable_chance
def crossover(cls, genome1, genome2):
child = cls([genome1, genome2])
child.nodes = copy(genome1.nodes)
for innovation, connection in genome1.connections.items():
disable_prob = 0.0
weight = None
if innovation in genome2.connections: # matching case
if not connection.enabled or not genome2.connections[
innovation].enabled:
disable_prob = DISABLE_PROB_IF_PARENT_DISABLED
# TODO: Parameterizing this might improve performance
if random.uniform(0, 1) < 0.5:
weight = connection.weight
else:
weight = genome2.connections[innovation].weight
else: # excess or disjoint case
disable_prob = 0.0 if connection.enabled else 1.0
weight = connection.weight
child._addConnection(
ConnectionGene(connection.in_, connection.out, weight,
disable_prob))
return child
def reproduce(self, generation):
baby_genome = None
if random.uniform(0, 1) < PERCENT_NO_CROSSOVER:
parent = self._select_org_for_reproduction()
baby_genome = parent.genome.clone()
baby_genome.verify()
baby_genome.mutate()
else:
parent1 = self._select_org_for_reproduction()
parent2 = self._select_org_for_reproduction()
parent1.genome.verify()
parent2.genome.verify()
if parent1.fitness > parent2.fitness:
baby_genome = Genome.crossover(parent1.genome, parent2.genome)
else:
baby_genome = Genome.crossover(parent2.genome, parent1.genome)
baby_genome.verify()
if parent1 is parent2 or random.uniform(0, 1) > MATE_ONLY_PROB:
baby_genome.mutate()
return Organism(generation, baby_genome)
def _mutate_any(self):
if not self.connections:
self.addConnectionMutation()
if random.uniform(0, 1) < MUTATE_WEIGHTS_PROB:
self.weightsMutation()
if random.uniform(0, 1) < MUTATE_ADD_LINK_PROB:
self.addConnectionMutation()
if random.uniform(0, 1) < MUTATE_ADD_NODE_PROB:
self.addNodeMutation()
if random.uniform(0, 1) < MUTATE_DELETE_LINK_PROB:
self.deleteConnectionMutation()
if random.uniform(0, 1) < MUTATE_TOGGLE_ENABLE_PROB:
self.toggleEnableMutation()
if random.uniform(0, 1) < MUTATE_RENABLE_PROB:
self.renenableMutation()
def _select_org_for_reproduction(self):
orgs = len(self.organisms)
favoured_ind = int(orgs - sqrt(orgs**2 - random.uniform(0, orgs**2)))
return self.organisms[favoured_ind]