def test_mutate(self):
c1 = create_chromosome(6)
c2 = create_chromosome(7)
c3 = create_chromosome(8)
assert mutate(copy.deepcopy(c1)) != c1
assert mutate(copy.deepcopy(c2)) != c2
def evolve(self):
# create a new population
new_population = Population(self.helper,
pop=self.POPULATION_SIZE,
tour=self.TOURNAMENT_SIZE,
cross=self.CROSSOVER_RATE,
mut=self.MUTATION_INVERSION_RATE)
# the best fitting solution go to the next population automatically (ELITISM)
best_fit = ga.get_best_fitness(self.chromosomes, self.helper)
elit_chromosome = Chromosome()
elit_chromosome.path = list(best_fit.path)
elit_chromosome.trucks_used = list(best_fit.trucks_used)
new_population.chromosomes.append(elit_chromosome)
elit_unchanged = Chromosome()
elit_unchanged.path = list(best_fit.path)
elit_unchanged.trucks_used = list(best_fit.trucks_used)
# crossover
for _ in range((self.POPULATION_SIZE - 2) / 2):
#print 'generating child ' + str(i)
# must select parent 1 using TOURNAMENT SELECTION
parent_1 = ga.tournament_selection(self.chromosomes, self.helper,
self.TOURNAMENT_SIZE)
# must select parent 2 using TOURNAMENT SELECTION
parent_2 = ga.tournament_selection(self.chromosomes, self.helper,
self.TOURNAMENT_SIZE)
# crossover these chomosomes
child = ga.crossover(parent_1, parent_2, self.helper,
self.CROSSOVER_RATE)
child.fitness = None # reset the fitness (it was calculated in the tournament and yet can mutate)
child.deposit_distance = None
new_population.chromosomes.append(child)
child2 = ga.crossover(parent_2, parent_1, self.helper,
self.CROSSOVER_RATE)
child2.fitness = None # reset the fitness (it was calculated in the tournament and yet can mutate)
child2.deposit_distance = None
new_population.chromosomes.append(child2)
# mutate
ga.mutate(new_population.chromosomes, self.helper,
self.MUTATION_INVERSION_RATE)
# insert one unchanged elit (to keep the best if its the case)
new_population.chromosomes.append(elit_unchanged)
if len(new_population.chromosomes) != self.POPULATION_SIZE:
print 'POPULATION WITH DFF SIZE'
return new_population
def generate(population, fitness):
mutation = 0.1
new_population = []
sort_population = sortPopulation(population,total_clashes,n_queens)
new_population = [x[0] for x in sort_population[:5000]]
for i in range(len(new_population)):
child = crossover(random.choice(new_population[:5000]),random.choice(new_population[:5000]))
if random.random() < mutation:
child = mutate(child)
new_population.append(child)
if fitness(child,total_clashes) == total_clashes:
break
new_population = sortPopulation(new_population,total_clashes,n_queens)
return [x[0] for x in new_population]
# Compute stats
avg_targets.append(sum(targets) / float(len(targets)))
best_targets.append(min(targets))
print("Best\t", best_targets[-1])
print("Worst\t", max(targets))
print("Mean\t", avg_targets[-1])
print("Variance", np.var(targets))
print()
# Compute next generation
parents = ga.select_mating_pool(population, targets, N_PARENTS)
offspring = ga.crossover(
parents,
offspring_size=(population.shape[0] - parents.shape[0],
population.shape[1]))
offspring = ga.mutate(offspring, MUTATION_RATE)
population[:N_PARENTS] = parents
population[N_PARENTS:] = offspring
# Put back into matrix form
population = [ann.vec_to_mat(**ann_params, vec=vec) for vec in population]
time_passed = time.time() - start
print('Generation took {0:.4f} seconds.'.format(time_passed))
# Save population to disk
with open("ga_population.csv", mode='w') as file:
writer = csv.writer(file, delimiter=',')
for i in population:
writer.writerow([i])
# Plot average target values
f"Generation: {gen}, Avg Fitness: {avg_fitness}, Avg Games Won: {avg_games}, Max Fitness: {population[0].fitness}, Max Games Won: {max_games_won}"
)
# Alter mutation intensity based on closeness to maximum solution (i.e. big mutations at start, small when nearing solution)
current_mutation_delta = mutation_delta * (1 -
(max_games_won / max_games))
#current_mutation_delta = mutation_delta * (1 - (gen / generations))
parents = ga.select_mating_pool(population, len(population) // 2)
children = ga.crossover(parents,
population_size - len(parents),
10,
ga.intermediate_crossover,
crossover_rate=0.5,
extra_range=0.25)
children = ga.mutate(children, mutation_rate, current_mutation_delta)
population = np.append(parents, children)
try:
with open("500-smartgreedy.csv", 'w') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=csv_header)
writer.writeheader()
for data in dict_data:
writer.writerow(data)
except IOError:
print("I/O error")
population = sorted(population,
key=lambda player: player.fitness,
reverse=True)