def create_next_generation(self):
parameters_list = [
game.snake.brain.parameters for game in self.current_generation
]
mating_pool = [
ga.tournament_selection(parameters_list, self.fitness_list)
for _ in range(len(parameters_list))
]
nn_architecture = self.current_generation[0].snake.brain.architecture
parent_chromosomes = [
ga.flatten_parameters(brain, nn_architecture)
for brain in mating_pool
]
crossed_chromosomes = []
for pair in range(0, len(parent_chromosomes), 2):
for child in ga.crossover(parent_chromosomes[pair],
parent_chromosomes[pair + 1],
self.app.crossover_rate):
child = ga.mutation(child, self.app.mutation_rate)
crossed_chromosomes.append(child)
# reshape parameters in the neural network architecture
new_parameters = []
for chromosome in crossed_chromosomes:
new_parameters.append(
ga.reshape_parameters(chromosome, nn_architecture))
self.generation_number += 1
self.current_generation = [
Game(self.app.surface, parameters=parameters)
for parameters in new_parameters
]
def generations(
population, pop_size, fitness_func, prob_cross, prob_muta,
select_parents, muta_method, cross_method, select_survivors,
max_gener, sizes, max_size, bests_matrix, averages_matrix, current_generation, current_run):
for gener in range(max_gener):
mates = select_parents(population, pop_size, 3)
offspring = crossover(mates, prob_cross, cross_method)
offspring = mutation(offspring, prob_muta, muta_method)
offspring = eval_pop(offspring, fitness_func, sizes, max_size)
population = select_survivors(population, offspring)
evaluate_generation(
population, bests_matrix, averages_matrix, current_generation +gener , current_run)
return population
def generations(population, pop_size, fitness_func, prob_cross, prob_muta,
select_parents, muta_method, cross_method, select_survivors,
max_gener, sizes, max_size, bests_matrix, averages_matrix,
current_generation, current_run):
for gener in range(max_gener):
mates = select_parents(population, pop_size, 3)
offspring = crossover(mates, prob_cross, cross_method)
offspring = mutation(offspring, prob_muta, muta_method)
offspring = eval_pop(offspring, fitness_func, sizes, max_size)
population = select_survivors(population, offspring)
evaluate_generation(population, bests_matrix, averages_matrix,
current_generation + gener, current_run)
return population
input('\n----- press ENTER to start ----- ')
population = GA.population(places, population_size, verbose)
for i in range(number_of_rouds):
population = GA.selection_and_crossover(
population,
number_of_parents,
lambda x: GA.hs_fitness(x, origin),
verbose
)
population = GA.mutation(
population,
number_of_parents,
probability_of_mutation,
verbose
)
route = GA.best_chromossome(
population,
lambda x: GA.hs_fitness(x, origin),
verbose
)
GA.print_route(route, origin)
while len(route) > 1:
total_remaining_distance = GA.hs_fitness(route, origin)
recommendation = FUZZY.simulate(fuel_level, total_remaining_distance)
fuel_level -= 10
print('\nROUTE CALCULATED RISK:', recommendation)
import genetic_algorithm
image_name = 'C:\\Users\\Dell\\Desktop\\My disk\\Sketches\\init7.jpg'
final_name = 'C:\\Users\\Dell\\Desktop\\My disk\\Sketches\\final7.jpg'
iterations_num = 30
genetic_algorithm.generate_init_pop(image_name)
print('End of initial population\'s generation')
for i in range(1, iterations_num + 1):
genetic_algorithm.fitness_function()
genetic_algorithm.selection_function()
genetic_algorithm.crossover()
genetic_algorithm.mutation()
print('End of ' + str(i) + ' iteration')
result = genetic_algorithm.termination()
print('End of termination')
result.save_image(final_name)
chromosome.fitness = pacman.evasion # 적합도는 유령 회피 성공 수
wr.writerow([n_gen, i, pacman.evasion, EndT,
score]) # csv 파일에 게임 결과 기록
if pgf.keyPressed('esc'):
n_gen = 0
break
if pgf.keyPressed('esc'):
break
# 유전 알고리즘을 이용한 진화 시작
if best_chromosomes is not None: # 퇴화 방지를 위해 이전 세대 상위 chromosome 까지 포함
chromosomes.extend(best_chromosomes)
chromosomes.sort(key=lambda x: x.fitness,
reverse=True) # 적합도를 기준으로 내림차순 정렬
print('===== Generaton #%s\tBest Fitness %s =====' %
(n_gen, chromosomes[0].fitness)) # 세대 끝, 최고 적합도 출력
best_chromosomes = deepcopy(
chromosomes[:N_BEST]) # 상위 chromosome 4개 따로 저장
ga.crossover(N_CHILDREN, best_chromosomes) # crossover
ga.mutation(N_POPULATION, N_BEST, N_CHILDREN, best_chromosomes,
PROB_MUTATION, chromosomes) #mutation
if pgf.keyPressed('esc'):
break
num_generations = 15
for generation in range(num_generations):
print("Generation : ", generation)
# Measing the fitness of each chromosome in the population.
fitness = GA.cal_pop_fitness(equation_inputs, new_population)
# Selecting the best parents in the population for mating.
parents = GA.select_mating_pool(new_population, fitness,
num_parents_mating)
# Generating next generation using crossover.
offspring_crossover = GA.crossover(
parents, offspring_size=(pop_size[0] - parents.shape[0], num_weights))
# Adding some variations to the offsrping using mutation.
offspring_mutation = GA.mutation(offspring_crossover)
# Creating the new population based on the parents and offspring.
new_population[0:parents.shape[0], :] = parents
new_population[parents.shape[0]:, :] = offspring_mutation
# The best result in the current iteration.
print("Best result : ",
numpy.max(numpy.sum(new_population * equation_inputs, axis=1)))
# Getting the best solution after iterating finishing all generations.
#At first, the fitness is calculated for each solution in the final generation.
fitness = GA.cal_pop_fitness(equation_inputs, new_population)
# Then return the index of that solution corresponding to the best fitness.
best_match_idx = numpy.where(fitness == numpy.max(fitness))
import variables
from genetic_algorithm import (crossover, fitness, get_initial_generation,
get_next_generation, get_total_distance,
mutation)
from utils import get_named_route, reached_stability
from variables import GENERATION_MAX
generation = get_initial_generation()
print('#gen\tmin\tavg')
for cnt in range(GENERATION_MAX):
generation = fitness(generation)
print(
f'{cnt}\t{get_total_distance(generation[0])}\t{variables.avg_distance}'
)
if reached_stability(generation):
break
new_individuals = crossover(generation)
new_individuals.extend(mutation(generation))
if cnt != GENERATION_MAX - 1:
generation = get_next_generation(generation, new_individuals)
print('best route:')
print(get_named_route(generation[0]))
print('with total distance:', get_total_distance(generation[0]))
def run(self):
# przypisanie do tablicy gorup_of_melody wszystkich wygenerowanych linii melodyczbych
for child in range(self.population):
self.generation.append(Child(self.tacts_number, self.scale))
for child in self.generation:
self.group_of_melody.append(child.melody)
self.plot_y_max = []
self.plot_y_mean = []
for iteration in range(self.iterations):
self.next_generation = []
self.valuation_probability = []
# ocena wszystkich linii melodycznych
self.valuation, self.valuation_probability, self.max_rate, self.medium_rate, self.final_music_argument, \
self.final_music_value = rating_music(self.group_of_melody.copy(), self.final_music_argument,
self.final_music_value)
self.plot_y_max.append(self.max_rate)
self.plot_y_mean.append(self.medium_rate)
for sub_iteration in range(self.population):
self.melody = []
# wybranie dwóch najlepiej przystosowanych osobników z zadanym prawdopodobieństwem
self.cross_two_line_arg = []
self.cross_two_line_arg = chosen_two(
self.valuation_probability.copy())
# krzyżowanie
flag = np.random.choice([0, 1],
p=[(1 - self.crossing_probability),
self.crossing_probability])
if flag:
self.melody = crossing(
self.group_of_melody[
self.cross_two_line_arg[0]].copy(),
self.group_of_melody[
self.cross_two_line_arg[1]].copy(),
self.crossing_type)
else:
x = np.random.choice(len(self.group_of_melody))
self.melody = self.group_of_melody[x].copy()
# mutacja
flag = np.random.choice([0, 1],
p=[(1 - self.mutation_probability),
self.mutation_probability])
if flag:
self.melody = mutation(self.melody.copy(), self.scale,
self.tacts_number).copy()
# przypisanie do następnej generacji przetworzonej melodii
self.next_generation.append(self.melody.copy())
# zastąpienie starej generacji nową
self.group_of_melody = self.next_generation.copy()
# dodanie podkłądu do stworzonej linii
back = []
chord_progression = [self.tonika, self.subdominata, self.dominata]
for chord in range(self.tacts_number):
if chord % 2 == 0:
back.append(chord_progression[int(chord / 2) % 3])
back.append(self.tonika)
# zapisanie wytworzonej muzyki
self.save = SaveMIDI(self.tempo, self.instrument, self.background,
self.file_path)
self.save.write_midi(self.final_music_argument)
self.save.write_background(back)
self.save.save_midi()
# zapisywanie tabulatury
for tact in self.final_music_argument:
for note in tact:
self.melody_to_tab.append(note)
save_tab(self.melody_to_tab, self.file_path)
# czyszczenie tablic
self.scale = []
self.generation = []
self.next_generation = []
self.melody = []
self.group_of_melody = []
self.valuation = []
self.valuation_probability = []
self.melody_to_tab = []
self.victory_melody = []
self.final_music_value = 0
self.final_music_argument = []
# wyświetlenie danych statystycznych
if self.checkbox:
plt.figure(1)
plt.plot([x for x in range(self.iterations)], self.plot_y_max, 'r',
[x for x in range(self.iterations)], self.plot_y_mean,
'b')
plt.title('Oceny utworów z funkcji przystosowania')
plt.xlabel('Generacja')
plt.ylabel('Ocena')
red_patch = mpatches.Patch(color='red', label='Maksimum')
blue_patch = mpatches.Patch(color='blue', label='Średnia')
plt.legend(handles=[red_patch, blue_patch])
print("Done")
# Selecting the best parents in the population for mating.
parents1 = genetic_algorithm.select_mating_pool(
stock_values, fit_population, index, num_parents_mating, T,
lamda, alpha, epsilon, delta, gamma)
parents2 = genetic_algorithm.select_mating_pool_unfit(
stock_values, unfit_population, num_parents_mating, T,
current_portofolio, starting_time, epsilon, delta, gamma)
# Generating next generation using crossover.
offspring_crossover_fit, offspring_crossover_unfit = genetic_algorithm.crossover(
parents1, parents2, index, stock_values, T, lamda, alpha,
epsilon, delta, gamma, current_portofolio, starting_time,
Kappa)
# Adding some variations to the offspring using mutation.
offspring_mutation_fit = genetic_algorithm.mutation(
offspring_crossover_fit, sigma, mutation_chance, Kappa)
offspring_mutation_unfit = genetic_algorithm.reverse_mutation(
offspring_crossover_unfit, sigma, mutation_chance)
if generation % 5 == 0 and generation > 0:
sigma = max(
0,
sigma * float((numpy.exp(
genetic_algorithm.gaussian_mutation(0, 1)))))
# Creating the new population based on the parents and offspring.
fit_population = genetic_algorithm.new_populations_fit(
fit_population, offspring_mutation_fit, stock_values, index, T,
current_portofolio, starting_time, lamda, alpha, epsilon,
delta, gamma)
