def test_worst_individual():
np.random.seed(42)
pop = pg.make_integer_population(10, 5)
pop = pg.evaluate_population(pop, lambda x: 1. / (1. + pg.onemax(x)))
worst = pg.worst_individual(pop)
assert np.array_equal(worst.genotype, np.array([0, 1, 0, 0, 0]))
assert worst.fitness.value == 0.5
def test_best_individual():
np.random.seed(42)
pop = pg.make_integer_population(10, 5)
pop = pg.evaluate_population(pop, lambda x: 1. / (1. + pg.onemax(x)))
best = pg.best_individual(pop)
assert np.array_equal(best.genotype, np.array([1, 1, 1, 1, 1]))
assert best.fitness.value == 0.16666666666666666
def test_elite_strategy():
np.random.seed(42)
size = 10
ind_size = 20
pop = pg.make_integer_population(size, ind_size)
fitness_function = lambda x: 1. / pg.onemax(x)
pop = pg.make_integer_population(size, ind_size)
pop = pg.evaluate_population(pop, fitness_function)
best = pg.best_individual(pop)
pop = pg.elite_strategy(pop, best)
found = False
for ind in pop.individuals:
if ind.equal(best) is True:
found = True
break
assert found is True
def test_select_population():
np.random.seed(42)
pop = pg.make_integer_population(10, 5)
pop = pg.evaluate_population(pop, lambda x: 1. / (1. + pg.onemax(x)))
diff = 0
for i in range(pop.size):
if not np.array_equal(pop.individuals[i].genotype,
pop.individuals[i].genotype):
diff += 1
assert diff == 0
original_pop = pop.clone()
pop = pg.select_population(pop, pg.tournament_selection)
for i in range(pop.size):
if not np.array_equal(pop.individuals[i].genotype,
original_pop.individuals[i].genotype):
diff += 1
assert diff != 0
def test_onemax():
assert pg.onemax(np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])) == 10
assert pg.onemax(np.array([1, 0, 1, 0, 1, 0, 1, 0, 1, 0])) == 5
assert pg.onemax(np.array([0, 0, 1, 1, 0, 0, 1, 0, 0, 0])) == 3
assert pg.onemax(np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) == 0
def fitness_fn(x):
return 1. / pg.onemax(x)