def test_best_individual_returned(one_island):
generator = MultipleValueChromosomeGenerator(generate_zero,
VALUE_LIST_SIZE)
best_indv = generator()
one_island.load_population([best_indv], replace=False)
archipelago = SerialArchipelago(one_island)
assert archipelago.test_for_convergence(error_tol=ERROR_TOL)
assert archipelago.get_best_individual().fitness == 0
def island():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(mutation_function)
selection = Tournament(10)
fitness = MultipleValueFitnessFunction()
evaluator = Evaluation(fitness)
ev_alg = MuPlusLambda(evaluator, selection, crossover, mutation, 0.2, 0.4,
20)
generator = MultipleValueChromosomeGenerator(mutation_function, 10)
return Island(ev_alg, generator, 25)
def test_multiple_indviduals_added_to_pop(init_replication_variation,
weaker_population):
indvs_added = 2
generator = MultipleValueChromosomeGenerator(
true_multiple_variation_function, COMPLEX_INDV_SIZE)
rand_indv_var_or = AddRandomIndividualVariation(init_replication_variation,
generator,
num_rand_indvs=indvs_added)
offspring = rand_indv_var_or(weaker_population, POP_SIZE)
count = 0
for indv in offspring:
if all(indv.values):
count += 1
assert count == indvs_added
def test_age_fitness_ea_step(pareto_front_population, onemax_evaluator,
selected_indiviudals):
population = pareto_front_population + selected_indiviudals
mutation = DumbyMutation()
crossover = DumbyCrossover()
generator = MultipleValueChromosomeGenerator(return_false,
COMPLEX_INDV_SIZE)
evo_alg = AgeFitnessEA(onemax_evaluator,
generator,
crossover,
mutation,
0,
0,
len(pareto_front_population),
selection_size=2 * len(population))
new_population = evo_alg.generational_step(population)
assert len(new_population) == len(population)
def population():
generator = MultipleValueChromosomeGenerator(mutation_onemax_specific, 10)
return [generator() for i in range(25)]
def true_chromosome_generator():
return MultipleValueChromosomeGenerator(true_variation_function,
SIMPLE_INDV_SIZE)
def weaker_population():
generator = MultipleValueChromosomeGenerator(false_variation_function,
COMPLEX_INDV_SIZE)
return [generator() for i in range(25)]
def unfit_generator():
return MultipleValueChromosomeGenerator(return_false, 10)
def weak_individual():
generator = MultipleValueChromosomeGenerator(return_false,
SIMPLE_INDV_SIZE)
indv = generator()
indv.genetic_age = 100
return indv
def create_chromosome_generator():
return MultipleValueChromosomeGenerator(generate_0_or_1,
values_per_chromosome=16)
def population():
generator = MultipleValueChromosomeGenerator(mutation_function, 10)
return [generator() for _ in range(25)]
def generator():
return MultipleValueChromosomeGenerator(np.random.random, 10)
def test_generator():
generator = MultipleValueChromosomeGenerator(mutation_onemax_specific, 10)
pop = [generator() for i in range(20)]
assert len(pop) == 20
assert len(pop[0].values) == 10
def mixed_fit_generator():
return MultipleValueChromosomeGenerator(mutation_function, 10)
def fit_individual():
generator = MultipleValueChromosomeGenerator(return_true, SIMPLE_INDV_SIZE)
indv = generator()
return indv
def three_island(evol_alg):
generator = MultipleValueChromosomeGenerator(generate_three,
VALUE_LIST_SIZE)
return Island(evol_alg, generator, POP_SIZE)
def strong_population():
generator = MultipleValueChromosomeGenerator(return_true, SIMPLE_INDV_SIZE)
return [generator() for _ in range(INITIAL_POP_SIZE)]
def island(evol_alg):
generator = MultipleValueChromosomeGenerator(mutation_function,
VALUE_LIST_SIZE)
return Island(evol_alg, generator, POP_SIZE)
def weak_population():
generator = MultipleValueChromosomeGenerator(return_false,
2 * SIMPLE_INDV_SIZE)
return [generator() for _ in range(INITIAL_POP_SIZE)]
def fit_generator():
return MultipleValueChromosomeGenerator(return_true, 10)