def test_chromosome():
for _ in range(10):
p1 = Chromosome(gen_prob=0.1, mutation_prob=0)
p2 = Chromosome(gen_prob=0.1, mutation_prob=0)
print("{} {}".format(p1.root, p1.eval()))
print("{} {}".format(p2.root, p2.eval()))
p1.crossover(p2)
print("{} {}".format(p1.root, p1.eval()))
print("{} {}".format(p2.root, p2.eval()))
print()
print()
def iteration(self, i):
parents = range(self.nrInd)
nrChildren = len(parents) // 2
offspring = Population(nrChildren)
for i in range(nrChildren):
offspring.individuals[i] = Chromosome.crossover(self.population.individuals[i << 1],
self.population.individuals[(i << 1) | 1],
self.probability_crossover)
offspring.individuals[i].mutate(self.probability_mutate)
offspring.evaluate(self.inputTrain, self.outputTrain)
self.population.reunion(offspring)
self.population.selection(self.nrInd)
def iteration(self):
parents = range(self.nrInd)
nrChildren = len(parents) // 2
offspring = Population(nrChildren, self.inputTrain, self.outputTrain)
# __str__ of population return the chromosomes fitness
for i in range(nrChildren):
offspring.individuals[i] = Chromosome.crossover(
choice(self.population.individuals),
choice(self.population.individuals), self.inputTrain,
self.outputTrain)
offspring.individuals[i].mutate(self.probability_mutate)
offspring.evaluate(self.inputTrain, self.outputTrain)
self.population.reunion(offspring)
self.population.selection(self.nrInd)
def evolve(self):
new_population = []
elites = self.get_fittest(self.population, self.elites)
new_population.extend(elites)
for _ in range((len(self.population) - self.elites) // 2):
tournament = random.sample(self.population, random.randint(2, 2))
winners = self.get_fittest(tournament, 2)
p1 = winners[0]
p2 = winners[1]
c1, c2 = Chromosome.crossover(p1, p2, self.crossover_probability)
c1.intra_depot_mutation(self.intra_depot_probability)
c2.intra_depot_mutation(self.intra_depot_probability)
c1.inter_depot_mutation(self.inter_depot_probability)
c2.inter_depot_mutation(self.inter_depot_probability)
new_population.extend([c1, c2])
self.population = new_population
def iteration(self, i):
offspring = Population(self.nrOfIndividuals)
for i in range(self.nrOfIndividuals):
# select two parents for crossover
first_pick = random.randint(0, self.nrOfIndividuals)
second_pick = random.randint(0, self.nrOfIndividuals)
while second_pick == first_pick:
second_pick = random.randint(0, self.nrOfIndividuals)
offspring.individuals[i] = Chromosome.crossover(
self.population.individuals[first_pick],
self.population.individuals[second_pick],
self.crossoverProbability)
offspring.individuals[i].mutate(self.mutationProbability)
offspring.evaluate(self.inputTraining, self.outputTraining)
self.population.join(offspring)
self.population.selection(self.nrOfIndividuals)
def __run(self):
self.__population = self.__generate_initial_population(
self.__initial_population_size)
generation = 0
while generation <= self.__num_generations:
c1, i1 = self.__select()
c2, i2 = self.__select(i1)
print("Best fitness: ", self.__best_fitness)
c3, c4 = Chromosome.crossover(c1, c2)
c5, c6 = c1.mutate(), c2.mutate()
cands = [c3, c4, c5, c6]
if c1.get_fitness() > c2.get_fitness():
max_fitness, second_fitness = c1.get_fitness(), c2.get_fitness(
)
best_cand, second_cand = c1, c2
else:
max_fitness, second_fitness = c2.get_fitness(), c1.get_fitness(
)
best_cand, second_cand = c2, c1
for cand in cands:
fitness = cand.get_fitness()
if fitness > max_fitness:
second_fitness = max_fitness
max_fitness = fitness
second_cand = best_cand
best_cand = cand
elif fitness > second_fitness:
second_fitness = fitness
second_cand = cand
self.__population[i1] = best_cand
self.__population[i2] = second_cand
if not (best_cand == c1 and second_cand == c2
or best_cand == c2 and second_cand == c1):
self.__cached_total_fitness = -1
generation += 1
def crossover(c1: Chromosome, c2: Chromosome) -> typing.List[Chromosome]:
child = Chromosome.crossover(c1, c2)
return child
