def tournament_selection(population):
offspring = []
select_parent = lambda x: random.randrange(x)
population_size = len(population)
for i in range(population_size):
parent_one = population[select_parent(population_size)]
parent_two = population[select_parent(population_size)]
parent_one = Individual(_id=parent_one.uid,
chromosome=parent_one.chromosome.copy(),
fitness=parent_one.fitness)
parent_two = Individual(_id=parent_two.uid,
chromosome=parent_two.chromosome.copy(),
fitness=parent_two.fitness)
if (parent_one.fitness > parent_two.fitness):
offspring.insert(i, parent_one)
elif (parent_one.fitness < parent_two.fitness):
offspring.insert(i, parent_two)
else:
choice = random.randint(0, 1)
if (choice):
offspring.insert(i, parent_one)
else:
offspring.insert(i, parent_two)
return offspring
def floating_point_boundary_mutate(individual: Individual,
mutation_probability: float,
rule_size: int):
idx = -1
for gene in individual.chromosome:
idx += 1
bound_idx = 0
chance = random.uniform(0, 1)
mutate = (chance <= mutation_probability)
if (not isinstance(gene, tuple)):
if (mutate):
gene ^= 1
individual.chromosome[idx] = gene
continue
bounds = []
for bound in gene:
chance = random.uniform(0, 1)
mutate = (chance <= mutation_probability)
bounds.append(bound)
if (mutate):
operator = -1 if random.randint(0, 1) else 1
mutation = random.uniform(0.01, 0.15) * operator
bounds[bound_idx] = mutate_bound(bound, mutation)
bound_idx += 1
individual.chromosome[idx] = (min(bounds), max(bounds))
def create_some_individuals(pg):
if pg.connection:
individuals = []
fake = Faker()
for i in range(0, random.randint(150, 400)):
aux = Individual(fake.name(), fake.address(),
random.randint(1, 75),
round(random.uniform(1, 2.2), 2))
individuals.append(aux)
pg.insert_individuals(individuals)
def bit_flip_mutate(individual: Individual, mutation_probabilty: float):
idx = -1
for cell in individual.chromosome:
idx += 1
chance = random.uniform(0, 1)
mutate = (chance <= mutation_probabilty)
if (mutate):
cell ^= 1
individual.chromosome[idx] = cell
else:
continue
def rule_based_mutate(individual: Individual, mutation_probability: float,
rule_size: int):
idx = -1
for cell in individual.chromosome:
idx += 1
chance = random.uniform(0, 1)
mutate = (chance <= mutation_probability)
if (mutate):
possibilities = [0, 1, '#']
if ((idx + 1) % rule_size == 0):
possibilities = [0, 1]
possibilities.remove(cell)
cell = random.choice(possibilities)
individual.chromosome[idx] = cell
def __initialise_population(self):
for i in range(self._population_size):
chromosome = self._generate_chromosome()
individual = Individual(i, chromosome=chromosome, fitness=0)
self._population.insert(i, individual)
self._generation = 1
def _initialise_population(self):
for i in range(0, self._population_size):
chromosome = self._generate_chromosome()
self._population.insert(
i, Individual(uuid.uuid1().hex, chromosome, 0))