def mutate_genome(self, genome) -> StorageGenome:
analysis_genome = AnalysisGenome(self.gene_repository, genome)
if len(analysis_genome.edges) == 0:
return self.mutate_add_edge(analysis_genome, genome)
edge_or_vertex = ProbabilisticTools.weighted_choice(
[
(0, self.mutation_parameters["add_edge_probability"]),
(1, 1 - self.mutation_parameters["add_edge_probability"])
]
)
if edge_or_vertex == 0:
new_genome = self.mutate_add_edge(analysis_genome, genome)
else:
new_genome = self.mutate_add_node(analysis_genome, genome)
return self.mutate_perturb_weights(new_genome)
def mutate_add_edge(
self,
analysis_genome: AnalysisGenome,
storage_genome: StorageGenome
) -> StorageGenome:
random.seed()
starting_vertex = random.choice(list(analysis_genome.nodes))
if starting_vertex not in analysis_genome.edges.keys():
# if the chosen vertex has no outgoing edges (i.e. is a sink), every
# other vertex may be a possible endpoint
possible_endpoints = list(analysis_genome.nodes)
else:
possible_endpoints = []
for node in analysis_genome.nodes:
for target_node, _ in analysis_genome.edges[starting_vertex]:
if node != target_node:
# if there is no edge from the selected node to the cur-
# rent node, it is a possible endpoint
possible_endpoints.append(node)
endpoint = random.choice(possible_endpoints)
gene_id = self.gene_repository.get_gene_id_for_endpoints(
starting_vertex,
endpoint
)
gene_weight = random.random()
gene_enabled = ProbabilisticTools.weighted_choice(
[
(True, self.mutation_parameters["new_gene_enabled_probability"]),
(False, 1 - self.mutation_parameters["new_gene_enabled_probability"])
]
)
new_genome = StorageGenome(storage_genome)
new_genome.genes[gene_id] = (gene_enabled, gene_weight)
return new_genome