def test_function_represents_one_run_with_30_generations(bests_matrix, averages_matrix, max_gener, numb_runs,current_run):
import init_pop
import fitness
sizes = numpy.array([5, 8, 4, 11, 6, 12])
max_size = 20
pop_size = 10
cromo_size = len(sizes)
fitness_func = fitness.subset_fitness
initial_pop = init_pop.init_pop(pop_size, cromo_size, init_pop.cromo_bin)
population = fitness.eval_pop(initial_pop, fitness_func, sizes, max_size)
#generation 1...30 generations
for current_generation in range(max_gener):
evaluate_generation(population, bests_matrix, averages_matrix,current_generation, current_run )
# population is reinitialized to simulate that a generation changed
initial_pop = init_pop.init_pop(pop_size, cromo_size, init_pop.cromo_bin)
population = fitness.eval_pop(initial_pop, fitness_func, sizes, max_size)
def test_function_represents_one_run_with_30_generations(
bests_matrix, averages_matrix, max_gener, numb_runs, current_run):
import init_pop
import fitness
sizes = numpy.array([5, 8, 4, 11, 6, 12])
max_size = 20
pop_size = 10
cromo_size = len(sizes)
fitness_func = fitness.subset_fitness
initial_pop = init_pop.init_pop(pop_size, cromo_size, init_pop.cromo_bin)
population = fitness.eval_pop(initial_pop, fitness_func, sizes, max_size)
#generation 1...30 generations
for current_generation in range(max_gener):
evaluate_generation(population, bests_matrix, averages_matrix,
current_generation, current_run)
# population is reinitialized to simulate that a generation changed
initial_pop = init_pop.init_pop(pop_size, cromo_size,
init_pop.cromo_bin)
population = fitness.eval_pop(initial_pop, fitness_func, sizes,
max_size)
if __name__ == '__main__':
import init_pop
import fitness
import parent_selection
import crossover
sizes = array([5, 8, 4, 11, 6, 12])
max_size = 20
pop_size = 10
cromo_size = len(sizes)
fitness_func = fitness.subset_fitness
select_parents = parent_selection.tournament_sel
initial_pop = init_pop.init_pop(pop_size, cromo_size, init_pop.cromo_bin)
population = fitness.eval_pop(initial_pop, fitness_func, sizes, max_size)
mates = select_parents(population, pop_size, 3)
prob_cross = 0.8
cross_method = crossover.one_point_cross, crossover.uniform_cross
offspring = crossover.crossover(mates, prob_cross, cross_method[0])
offspring = fitness.eval_pop(offspring, fitness_func, sizes, max_size)
select_survivors = survivors_steady_state
population = select_survivors(population, offspring, 0.02)
#[print (i) for i in offspring]
print("pop:")
#[print (i) for i in population]
if __name__ == '__main__':
import init_pop
import fitness
import parent_selection
import crossover
sizes = array([5, 8, 4, 11, 6, 12])
max_size = 20
pop_size = 10
cromo_size = len(sizes)
fitness_func = fitness.subset_fitness
select_parents = parent_selection.tournament_sel
initial_pop = init_pop.init_pop(pop_size, cromo_size, init_pop.cromo_bin)
population = fitness.eval_pop(initial_pop, fitness_func, sizes, max_size)
mates = select_parents(population,pop_size,3)
prob_cross = 0.8
cross_method = crossover.one_point_cross, crossover.uniform_cross
offspring = crossover.crossover(mates, prob_cross, cross_method[0])
offspring = fitness.eval_pop(offspring,fitness_func, sizes, max_size)
select_survivors = survivors_steady_state
population = select_survivors(population, offspring, 0.02)
#[print (i) for i in offspring]
print ("pop:")
#[print (i) for i in population]