def test_tournament_selection2():
"""If there are just two individuals in the population, and we set select_worst=True,
then binary tournament selection will select the worse one with 75% probability."""
# Make a population where binary tournament_selection has an obvious
# reproducible choice
pop = [
Individual(np.array([0, 0, 0]), problem=MaxOnes()),
Individual(np.array([1, 1, 1]), problem=MaxOnes())
]
# Assign a unique identifier to each individual
pop[0].id = 0
pop[1].id = 1
# We first need to evaluate all the individuals so that
# selection has fitnesses to compare
pop = Individual.evaluate_population(pop)
selected = ops.tournament_selection(pop, select_worst=True)
N = 1000
p_thresh = 0.1
observed_dist = statistical_helpers.collect_distribution(
lambda: next(selected).id, samples=N)
expected_dist = {pop[0].id: 0.75 * N, pop[1].id: 0.25 * N}
print(f"Observed: {observed_dist}")
print(f"Expected: {expected_dist}")
assert (statistical_helpers.stochastic_equals(expected_dist,
observed_dist,
p=p_thresh))
def test_random_selection1():
"""If there are just two individuals in the population, then random
selection will select the better one with 50% probability."""
pop = [
Individual(np.array([0, 0, 0]), problem=MaxOnes()),
Individual(np.array([1, 1, 1]), problem=MaxOnes())
]
# Assign a unique identifier to each individual
pop[0].id = 0
pop[1].id = 1
# We first need to evaluate all the individuals so that
# selection has fitnesses to compare
pop = Individual.evaluate_population(pop)
selected = ops.random_selection(pop)
N = 1000
p_thresh = 0.1
observed_dist = statistical_helpers.collect_distribution(
lambda: next(selected).id, samples=N)
expected_dist = {pop[0].id: 0.5 * N, pop[1].id: 0.5 * N}
print(f"Observed: {observed_dist}")
print(f"Expected: {expected_dist}")
assert (statistical_helpers.stochastic_equals(expected_dist,
observed_dist,
p=p_thresh))
def test_sus_selection_shuffle():
''' Test of a stochastic case of SUS selection '''
# Make a population where sus_selection has an obvious
# reproducible choice
# Proportions here should be 1/4 and 3/4, respectively
pop = [
Individual(np.array([0, 1, 0]), problem=MaxOnes()),
Individual(np.array([1, 1, 1]), problem=MaxOnes())
]
# Assign a unique identifier to each individual
pop[0].id = 0
pop[1].id = 1
# We first need to evaluate all the individuals so that
# selection has fitnesses to compare
pop = Individual.evaluate_population(pop)
selected = ops.sus_selection(pop)
N = 1000
p_thresh = 0.1
observed_dist = statistical_helpers.collect_distribution(
lambda: next(selected).id, samples=N)
expected_dist = {pop[0].id: 0.25 * N, pop[1].id: 0.75 * N}
print(f"Observed: {observed_dist}")
print(f"Expected: {expected_dist}")
assert (statistical_helpers.stochastic_equals(expected_dist,
observed_dist,
p=p_thresh))