def breed_loop(N, data, gdata, save_interval):
ffts = []
min_errs = []
for n in range(N):
# print("Generation:", n)
errors, goal = gen.fit_all(data, gdata)
new_data = np.zeros(np.shape(data))
for i in range(len(data)//2):
if goal:
print("Goal!")
r = np.arange(len(errors))
i1, i2 = np.random.choice(r, 2, p=errors)
cross1, cross2 = gen.crossover(data[i1], data[i2])
cross1, cross2 = gen.mutate(cross1, get_temp(n+1)), gen.mutate(cross2, get_temp(n+1))
# cross1, cross2 = gen.mutate(cross1, .5), gen.mutate(cross2, .5)
new_data[i], new_data[i+(len(data)//2)] = cross1, cross2
if n % save_interval == 0 and i == 0:
play_fft(cross1)
data = new_data
if n % 25 == 0:
print(f'min error {np.amin(errors)}')
min_errs.append(np.amin(errors))
return ffts, min_errs
def main():
pop_size = 1000
max_runs = 50
mutate_ratio = 0.2
# Which data set to use.
mode = 1
random.seed()
pop = []
# Create a random population of trees.
for i in range(pop_size):
pop.append(ExprTree(mode))
print "Population created."
for tree in pop:
print tree
for i in range(max_runs):
print "Beginning crossover " + str(i) + ":"
# Crossover and mutate population.
genetic.mutate(pop, mutate_ratio, mode)
pop = genetic.crossover(pop, mode)
print "Crossover " + str(i) + " complete."
def main():
map_size = 100
p_type = 10 # dont get when we will use it
max_route_length = 150
population_size = 30
number_of_each_new_generation = 10
number_of_couples = int(
(population_size - number_of_each_new_generation) / 2)
probability = 0.05
the_map = initialize(p_type, map_size)
population = create_starting_population(max_route_length, population_size,
the_map)
last_distance = 1000000000
for i in range(0, 100):
scores = score_population(population, the_map)
new_population = []
best = population[np.argmin(scores)]
number_of_moves = len(best)
distance = fitness(best, the_map)
if distance != last_distance:
print(
'Iteration %i: Best so far is %i steps for a distance of %f' %
(i, number_of_moves, distance))
plot_best(the_map, best, i)
for j in range(0, number_of_couples):
new_route1, new_route2 = crossover(
population[pick_breeder(scores)],
population[pick_breeder(scores)])
new_population.extend([new_route1, new_route2])
# mutate the current members of new_population
for j in range(0, len(new_population)):
new_population[j] = mutate(new_population[j], probability, the_map)
new_population.append(population[np.argmin(scores)])
while len(new_population) < population_size:
new_population.append(create_new_member(max_route_length, the_map))
population = new_population[::] # copy a values
new_population)
print('Generation calculation time : ', time.time() - calculation_time)
best_outputs.append(np.max(fitness))
print('Number of creatures with best fitness : ',
(fitness == np.max(fitness)).sum())
# The best result in the current generation.
print("Best result : ", best_outputs[-1])
# Selecting the best parents for mating.
parents = genetic.select_mating_pool(new_population, fitness, n_parents)
# Generating next generation.
offspring_crossover = genetic.crossover(
parents,
offspring_size=(population_shape[0] - parents.shape[0], n_features))
# Adding some variations to the offspring using mutation.
offspring_mutation = genetic.mutation(offspring_crossover, n_mutations)
# Creating the new population based on the parents and offspring.
new_population[0:parents.shape[0], :] = parents
new_population[parents.shape[0]:, :] = offspring_mutation
# Getting the best solution after finishing all generations.
# At first, the fitness is calculated for each solution in the final generation.
fitness = genetic.pop_fitness(X_train, X_test, y_train, y_test, new_population)
# Then return the index of that solution corresponding to the best fitness.
best_match_idx = np.where(fitness == np.max(fitness))[0]
best_match_idx = best_match_idx[0]
######################################################################
# GA Program #3: Simulates crossover events between 2 chromosomes
######################################################################
import sys, os
sys.path.append(os.path.join(os.path.dirname(os.getcwd()), 'src'))
import genetic as g
c1 = g.Chromosome([0]*50, [1,2,3,4]) # integer chromosome
c2 = g.Chromosome(['A']*50, ['A','B','C','D']) # alphabet chromosome
print('INITIAL CHROMOSOMES ==================================')
print('Integer chromosome sequence: ' + str(c1.sequence))
print('Alphabet chromosome sequence: ' + str(c2.sequence))
for x in range(10, 50, 10):
print()
(g1, g2) = g.crossover(c1, c2, x)
print('Crossover at base ' + str(x))
print('Integer chromosome sequence: ' + str(g1.sequence))
print('Alphabet chromosome sequence: ' + str(g2.sequence))
######################################################################
# GA Program #3: Simulates crossover events between 2 chromosomes
######################################################################
import sys, os
sys.path.append(os.path.join(os.path.dirname(os.getcwd()), 'copads'))
import genetic as g
c1 = g.Chromosome([0] * 50, [1, 2, 3, 4]) # integer chromosome
c2 = g.Chromosome(['A'] * 50, ['A', 'B', 'C', 'D']) # alphabet chromosome
print('INITIAL CHROMOSOMES ==================================')
print('Integer chromosome sequence: ' + str(c1.sequence))
print('Alphabet chromosome sequence: ' + str(c2.sequence))
for x in range(10, 50, 10):
print()
(g1, g2) = g.crossover(c1, c2, x)
print('Crossover at base ' + str(x))
print('Integer chromosome sequence: ' + str(g1.sequence))
print('Alphabet chromosome sequence: ' + str(g2.sequence))
list_fitness.append(fitness)
list_objective.append(objective)
list_inputs.append(pop_inputs.tolist())
list_other_metrics.append(other_metrics)
# top performance ANN model in the population are selected for mating.
parents = genetic.mating_pool(pop_inputs, objective.copy(),
num_parents_mating)
print("Parents")
print(parents)
parents = numpy.asarray(parents)
# Crossover to generate the next geenration of solutions
offspring_crossover = genetic.crossover(
parents,
offspring_size=(int(num_parents_mating / 2), pop_inputs.shape[1]))
print("Crossover")
print(offspring_crossover)
# Mutation for population variation
offspring_mutation = genetic.mutation(offspring_crossover,
sol_per_pop,
num_parents_mating,
mutation_percent=mutation_percent)
print("Mutation")
print(offspring_mutation)
# New population for generation g+1
pop_inputs[0:len(offspring_crossover), :] = offspring_crossover
def crossover(i, j):
return [frombin(i) for i in genetic.crossover(tobin(i), tobin(j))]
while iter < MAX_NUM_ITER:
print('Generación Numero: ', iter)
poblacionDecimal = genetic.poblacionEnDecimal(poblacion, ORDEN_MATRIZ,
BITS_GEN)
#print(poblacionDecimal)
poblacionFit = genetic.poblacionFitness(poblacionDecimal, OBJETIVO)
print('Fitness de la Poblacion: ', poblacionFit)
poblacionEmparejada = genetic.generaParejas(poblacionFit, poblacion,
N_BITS)
#print(poblacionEmparejada)
poblacionCruzada = genetic.crossover(PUNTOS_CROSSOVER, poblacionEmparejada)
#print(poblacionCruzada)
poblacionNueva = genetic.mutacion(FACTOR_MUTACION, NUM_BITS_MUTADOS,
poblacionCruzada)
#print(poblacionNueva)
poblacion = poblacionNueva
print('Mejor fitness: ', min(poblacionFit))
print('Peor fitness: ', max(poblacionFit))
promedio = sum(poblacionFit) / len(poblacionFit)
print('Promedio: ', promedio)
if min(poblacionFit) == 0:
posicion = poblacionFit.index(min(poblacionFit))