def comma_algo(add_e_evaluation, add_v_variation, add_s_selection):
crossover = SinglePointCrossover()
mutation = SinglePointMutation(mutation_function)
evo_alg = MuCommaLambda(add_e_evaluation, add_s_selection, crossover,
mutation, 0.2, 0.4, 20)
evo_alg.variation = add_v_variation
return evo_alg
def test_just_replication(population):
crossover = SinglePointCrossover()
mutation = SinglePointMutation(mutation_function)
var_or_instance = VarOr(crossover, mutation, 0.0, 0.0)
_ = var_or_instance(population, 25)
for cross, mut in zip(var_or_instance.crossover_offspring,
var_or_instance.mutation_offspring):
assert not (cross or mut)
def evol_alg():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(NumberGenerator(-1))
selection = Tournament(SELECTION_SIZE)
fitness = MultipleValueFitnessFunction()
evaluator = Evaluation(fitness)
return MuPlusLambda(evaluator, selection, crossover, mutation, 0.2, 0.4,
OFFSPRING_SIZE)
def ev_alg(full_training_data):
crossover = SinglePointCrossover()
mutation = SinglePointMutation(np.random.random)
selection = Tournament(2)
fitness = DistanceToAverage(full_training_data)
evaluator = Evaluation(fitness)
return MuPlusLambda(evaluator, selection, crossover, mutation, 0., 1.0,
MAIN_POPULATION_SIZE)
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_mutation_is_single_point():
mutator = SinglePointMutation(mutation_onemax_specific)
parent = MultipleValueChromosome(
[np.random.choice([True, False]) for _ in range(10)])
child = mutator(parent)
discrepancies = 0
for i in range(len(parent.values)):
if child.values[i] != parent.values[i]:
discrepancies += 1
assert discrepancies <= 1
def create_evolutionary_algorithm():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(generate_0_or_1)
variation_phase = VarOr(crossover,
mutation,
crossover_probability=0.4,
mutation_probability=0.4)
fitness = OneMaxFitnessFunction()
evaluation_phase = Evaluation(fitness)
selection_phase = Tournament(tournament_size=2)
return EvolutionaryAlgorithm(variation_phase, evaluation_phase,
selection_phase)
def main():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(get_random_float)
selection = Tournament(10)
fitness = ZeroMinFitnessFunction()
local_opt_fitness = ContinuousLocalOptimization(fitness)
evaluator = Evaluation(local_opt_fitness)
ea = MuPlusLambda(evaluator, selection, crossover, mutation, 0.4, 0.4, 20)
generator = MultipleFloatChromosomeGenerator(get_random_float, 8)
island = Island(ea, generator, 25)
island.evolve(1)
report_max_min_mean_fitness(island)
island.evolve(500)
report_max_min_mean_fitness(island)
def test_island_hof(mocker):
hof = mocker.Mock()
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)
island = Island(ev_alg, generator, 25, hall_of_fame=hof)
island.evolve(10)
hof.update.assert_called_once()
hof_update_pop = hof.update.call_args[0][0]
for h, i in zip(hof_update_pop, island.population):
assert h == i
def test_invalid_probabilities():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(mutation_function)
with pytest.raises(ValueError):
_ = VarOr(crossover, mutation, 0.6, 0.41)
def var_or():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(mutation_function)
var_or_instance = VarOr(crossover, mutation, 0.2, 0.4)
return var_or_instance
def test_fitness_is_not_inherited_mutation():
mutator = SinglePointMutation(mutation_onemax_specific)
parent = MultipleValueChromosome(
[np.random.choice([True, False]) for _ in range(10)])
child = mutator(parent)
assert not child.fit_set
def dc_ea(onemax_evaluator):
crossover = SinglePointCrossover()
mutation = SinglePointMutation(return_true)
return DeterministicCrowdingEA(onemax_evaluator, crossover, mutation, 0.2,
0.2)