def mutate_add_node(self, genome: Genome) -> bool:
if len(genome.connection_gene_list) == 0:
return False
genome.last_mutation = "Add node"
connection_to_replace = numpy.random.choice(
genome.connection_gene_list)
genome.connection_gene_list.remove(connection_to_replace)
neuron_id = 0
if connection_to_replace.inno_id in self.connection_replaced:
neuron_id = self.connection_replaced[connection_to_replace.inno_id]
else:
neuron_id = self.next_neuron_innovation()
self.connection_replaced[connection_to_replace.inno_id] = neuron_id
new_neuron = NeuronGene(
neuron_id, self.activation_fn_library.get_random_function(),
NeuronType.HIDDEN)
self.neuron_innovation[new_neuron.inno_id] = new_neuron
new_connection1 = self.create_connection(connection_to_replace.from_id,
neuron_id,
connection_to_replace.weight)
new_connection2 = self.create_connection(neuron_id,
connection_to_replace.to_id,
1.0)
from_neuron = genome.neuron_gene_dict[connection_to_replace.from_id]
to_neuron = genome.neuron_gene_dict[connection_to_replace.to_id]
from_neuron.target_neurons.remove(to_neuron)
to_neuron.source_neurons.remove(from_neuron)
new_neuron.target_neurons.append(
genome.neuron_gene_dict[connection_to_replace.to_id])
new_neuron.source_neurons.append(
genome.neuron_gene_dict[connection_to_replace.from_id])
genome.neuron_gene_dict[new_neuron.inno_id] = new_neuron
genome.neuron_gene_list.append(new_neuron)
from_neuron.target_neurons.append(new_neuron)
to_neuron.source_neurons.append(new_neuron)
genome.connection_gene_list.append(new_connection1)
genome.connection_gene_list.append(new_connection2)
return True
def mutate_weights(self, genome: Genome) -> bool:
num_connection_mutation: float = numpy.sin(
random.random() *
(numpy.pi / 2)) * len(genome.connection_gene_list)
connections_to_mutate: List[ConnectionGene] = random.sample(
genome.connection_gene_list, int(num_connection_mutation))
for conn in connections_to_mutate:
conn.weight = numpy.clip(
conn.weight + (random.random() - 0.5),
-self.genome_params.connection_weight_range,
self.genome_params.connection_weight_range)
genome.last_mutation = "Mutate weights"
return num_connection_mutation > 0
def mutate_delete_connection(self, genome: Genome) -> bool:
if len(genome.connection_gene_list) < 2:
return False
connection_to_delete: ConnectionGene = random.choice(
genome.connection_gene_list)
from_neuron = genome.neuron_gene_dict[connection_to_delete.from_id]
to_neuron = genome.neuron_gene_dict[connection_to_delete.to_id]
from_neuron.target_neurons.remove(to_neuron)
to_neuron.source_neurons.remove(from_neuron)
genome.connection_gene_list.remove(connection_to_delete)
genome.last_mutation = "Delete"
return True
def mutate_add_connection(self, genome: Genome) -> bool:
neuron_count = len(genome.neuron_gene_list)
hidden_output_neuron_count = neuron_count - self.input_count
input_bias_hidden_neuron_count = neuron_count - self.output_count
genome.last_mutation = "Add"
if self.genome_params.feed_forward_only:
for attempts in range(5):
source_neuron_idx = random.randint(
0, input_bias_hidden_neuron_count - 1)
if self.input_count + self.output_count > source_neuron_idx >= self.input_count:
source_neuron_idx += self.output_count
target_neuron_idx = self.input_count + random.randint(
0, hidden_output_neuron_count - 1)
if source_neuron_idx == target_neuron_idx:
target_neuron_idx += 1
if target_neuron_idx == neuron_count:
continue
source_neuron = genome.neuron_gene_list[source_neuron_idx]
target_neuron = genome.neuron_gene_list[target_neuron_idx]
if target_neuron.type == NeuronType.BIAS or target_neuron in source_neuron.target_neurons or genome.is_connection_cyclic(
source_neuron.inno_id, target_neuron.inno_id):
continue
self.mutate_add_create_connection(genome, source_neuron,
target_neuron)
return True
else:
for attempts in range(5):
source_neuron_idx = random.randint(0, neuron_count - 1)
target_neuron_idx = self.input_count + random.randint(
0, hidden_output_neuron_count - 1)
source_neuron = genome.neuron_gene_list[source_neuron_idx]
target_neuron = genome.neuron_gene_list[target_neuron_idx]
self.mutate_add_create_connection(genome, source_neuron,
target_neuron)
return True
