def mutate(self) -> Individual:
chromosome = self.chromosome
satisfied = self.satisfied
no_mutation = gui_get('no_mutation')
move_unsatisfied = gui_get('move_unsatisfied_gene')
exchange_unsatisfied_genes = gui_get('exchange_unsatisfied_genes')
reverse_subseq = gui_get('reverse_subseq')
mutations_options = move_unsatisfied + exchange_unsatisfied_genes + reverse_subseq + no_mutation
mutation_choice = randint(0, mutations_options)
if mutation_choice <= move_unsatisfied:
unsatisfied_indices = [
i for i in range(len(satisfied)) if not satisfied[i]
]
if not unsatisfied_indices:
return self
new_chromosome = chromosome.move_unsatisfied_gene(
unsatisfied_indices)
elif mutation_choice <= move_unsatisfied + exchange_unsatisfied_genes:
new_chromosome = chromosome.exchange_unsatisfied_genes(satisfied)
elif mutation_choice <= move_unsatisfied + exchange_unsatisfied_genes + reverse_subseq:
new_chromosome = chromosome.reverse_subseq()
else:
return self
new_individual = GA_World.individual_class(new_chromosome)
return new_individual
def gen_individual():
chromosome_list: List = sample(GA_World.gene_pool,
Cycle_World.cycle_length)
Cycle_World.individuals += 1
individual = GA_World.individual_class(
GA_World.chromosome_class(chromosome_list))
return individual
def mutate(self) -> Individual:
if randint(0, 100) <= gui_get('invert selection'):
new_chromosome = self.chromosome.invert_a_gene()
new_individual = GA_World.individual_class(new_chromosome)
return new_individual
else:
return self
def gen_individual(self):
# Use ceil to ensure we have enough genes.
zeros = [0]*ceil(self.chromosome_length/2)
ones = [1]*ceil(self.chromosome_length/2)
mixture = sample(zeros + ones, self.chromosome_length)
chromosome_tuple: Tuple[Gene] = GA_World.chromosome_class(Gene(id, val) for (id, val) in zip(count(), mixture))
individual = GA_World.individual_class(chromosome_tuple)
return individual
def gen_individual():
path_methods = [TSP_Chromosome.random_path]
if gui_get('Greedy'):
path_methods.append(TSP_Chromosome.greedy_path)
if gui_get('Min spanning tree'):
path_methods.append(TSP_Chromosome.spanning_tree_path)
gen_path_method = choice(path_methods)
chromosome_list: List = gen_path_method()
chromo = GA_World.chromosome_class(chromosome_list)
individual = GA_World.individual_class(chromo, generator=gen_path_method, original_sequence=chromosome_list)
return individual
def mutate(self) -> Individual:
mutation_operations = []
chromo = self.chromosome
assert isinstance(chromo, TSP_Chromosome)
if gui_get('Move element'):
mutation_operations.append(chromo.move_gene_in_chromosome)
if gui_get('2-Opt'):
mutation_operations.append(chromo.two_opt)
# If no mutation operations have been selected, return the individual unchnged.
if not mutation_operations:
return self
else:
# Otherwise, apply one of the selected mutation operation.
operation = choice(mutation_operations)
# Both of the current mutation operations require self.fitness as their argument.
new_chromosome = operation(self.fitness)
new_individual = GA_World.individual_class(new_chromosome)
return new_individual
def gen_individual():
chromosome_tuple: Tuple[Gene] = GA_World.chromosome_class(
Gene(id, val) for (id, val) in zip(count(), GA_World.gene_pool))
individual = GA_World.individual_class(chromosome_tuple)
return individual
def gen_individual(self):
chromosome_list: List = sample(World.agents, self.cycle_length)
individual = GA_World.individual_class(
GA_World.chromosome_class(chromosome_list))
return individual
def mutate(self) -> Individual:
new_chromosome = (self.chromosome).replace_gene_in_chromosome(
self.fitness)
new_individual = GA_World.individual_class(new_chromosome)
return new_individual
