def test_get_pareto_front(pareto_front_population, selected_indiviudals,
onemax_evaluator):
selection = AgeFitness()
population = pareto_front_population + selected_indiviudals
onemax_evaluator(population)
new_population = selection.select_pareto_front(population)
assert collections.Counter(new_population) == \
collections.Counter(pareto_front_population)
def test_all_but_one_removed(all_dominated_population, onemax_evaluator):
age_fitness_selection = AgeFitness()
onemax_evaluator(all_dominated_population)
target_pop_size = 1
new_population = age_fitness_selection(all_dominated_population,
target_pop_size)
assert len(new_population) == target_pop_size
def test_none_selected_for_removal(non_dominated_population, onemax_evaluator):
age_fitness_selection = AgeFitness()
onemax_evaluator(non_dominated_population)
target_pop_size = 1
new_population = age_fitness_selection(non_dominated_population,
target_pop_size)
assert len(new_population) == len(non_dominated_population)
def test_all_removed_in_one_iteration(weak_individual, fit_individual,
onemax_evaluator):
population = [weak_individual for _ in range(10)] + [fit_individual]
onemax_evaluator(population)
age_fitness_selection = AgeFitness(selection_size=len(population))
target_pop_size = 1
new_population = age_fitness_selection(population, target_pop_size)
assert len(new_population) == target_pop_size
assert new_population[0].values == [True]
assert age_fitness_selection._selection_attempts == 1
def test_all_but_one_removed_large_selection_size(strong_population,
weak_individual,
onemax_evaluator):
population = strong_population + [weak_individual]
onemax_evaluator(population)
age_fitness_selection = AgeFitness(selection_size=len(strong_population))
target_pop_size = 1
new_population = age_fitness_selection(population, target_pop_size)
assert len(new_population) == target_pop_size
assert new_population[0].values == [True]
assert age_fitness_selection._selection_attempts == 2
def test_selection_size_larger_than_population(weak_population, fit_individual,
onemax_evaluator):
population = weak_population + [fit_individual]
onemax_evaluator(population)
age_fitness_selection = AgeFitness(selection_size=(len(population) + 100))
target_pop_size = 2
new_population = age_fitness_selection(population, target_pop_size)
assert len(new_population) == target_pop_size
assert age_fitness_selection._selection_attempts == 1
count = 1
for indv in new_population:
if not any(indv.values):
count *= 2
elif all(indv.values):
count *= 3
assert count == 6
def test_keep_pareto_front_miss_target_pop_size(pareto_front_population,
onemax_evaluator,
selected_indiviudals):
selected_indv_one = selected_indiviudals[0]
selected_indv_two = selected_indiviudals[1]
population = pareto_front_population + selected_indiviudals
onemax_evaluator(population)
age_fitness_selection = AgeFitness(selection_size=len(population))
new_population = age_fitness_selection(population, TARGET_POP_SIZE)
assert len(new_population) == len(pareto_front_population)
selected_indvs_removed = True
for indv in new_population:
if (indv.genetic_age == selected_indv_one and
indv.values == selected_indv_one.values) or \
(indv.genetic_age == selected_indv_two and
indv.values == selected_indv_two.values):
selected_indvs_removed = False
break
assert selected_indvs_removed
def test_target_population_size_is_valid(strong_population):
age_fitness_selection = AgeFitness()
with pytest.raises(ValueError):
age_fitness_selection(strong_population, len(strong_population) + 1)