def test_select_mating_pool_SUS():
np.random.seed(11)
evo = Evolution(
population_size=10,
population=np.array(list(range(1, 11))),
genotype_size=1,
fitness_normalization_mode='FPS',
selection_mode='SUS',
reproduction_mode='GENETIC_ALGORITHM',
evaluation_function=lambda _:
[0.3, 0.2, 0.1, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05],
max_generation=0,
elitist_fraction=0.2,
mating_fraction=0.8, # in beer this is 1 - elitist_fraction
)
evo.run()
evo.fitnesses = np.array(
[0.3, 0.2, 0.1, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05])
# print("Fitnesses: {}".format(evo.fitnesses))
assert evo.n_mating == 8
assert evo.n_elite == 2
assert evo.n_fillup == 0
mating_pool = evo.select_mating_pool()
print(mating_pool)
assert mating_pool == [1, 1, 1, 2, 3, 4, 6, 8]
print('SUCCESS!')
def test_select_mating_pool_RWS():
from collections import Counter
performances = np.array([1000, 1000, 300, 200, 200, 100, 50, 50, 50, 50])
np.random.seed(11)
evo = Evolution(
population_size=10,
population=np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
genotype_size=1,
fitness_normalization_mode='RANK',
selection_mode='RWS',
evaluation_function=lambda _: performances,
max_generation=0,
elitist_fraction=0.2,
mating_fraction=0.8, # in beer this is 1 - elitist_fraction
)
evo.run()
print("Fitnesses: {}".format(evo.fitnesses))
# assert evo.n_elite == 2 # only in genetic algorithm
# assert evo.n_fillup == 0 # only in genetic algorithm
assert evo.n_mating == evo.population_size
mating_counter = Counter()
repetitions = 10000
for _ in range(repetitions):
mating_pool = evo.select_mating_pool()
for e in mating_pool:
mating_counter[e] += 1
# we check if the number of time each genome is selected is proportionate to the its performance (and fitness)
# not guaranteed to work for fitness_normalization_mode == 'FPS' because of the linear scaling
# not easy to test this further (not implemented in beer)
for i in range(2, 9):
assert mating_counter[i] > mating_counter[i + 1]
print(sorted(mating_counter.items(), key=lambda kv: kv[0]))
print('SUCCESS!')