def test_GeneticAlgorithm_08():
random.seed(89226)
ga = auxjad.GeneticAlgorithm(
target=['A', 'B', 'C', 'D', 'E'],
genes=['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],
)
ga.__next__()
assert ga.fittest_individual == ['A', 'C', 'D', 'D', 'D']
ga.__next__()
assert ga.fittest_individual == ['A', 'C', 'E', 'D', 'E']
next(ga)
assert ga.fittest_individual == ['A', 'C', 'E', 'D', 'E']
next(ga)
assert ga.fittest_individual == ['A', 'C', 'D', 'D', 'E']
def test_GeneticAlgorithm_01():
ga = auxjad.GeneticAlgorithm(
target=['A', 'B', 'C', 'D', 'E'],
genes=['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],
)
assert format(ga) == "['A', 'B', 'C', 'D', 'E']"
assert len(ga) == 5
assert ga.initial_individual is None
assert ga.population_size == 100
assert ga.select_n_parents == 10
assert ga.mutation_chance == 0.2
assert ga.mutation_index == 0.1
assert ga.evaluation_index == 0.2
assert ga.target == ['A', 'B', 'C', 'D', 'E']
def test_GeneticAlgorithm_05():
random.seed(51902)
ga = auxjad.GeneticAlgorithm(
target=['A', 'B', 'C', 'D', 'E'],
genes=['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],
)
assert ga.fittest_individual is None
assert ga.generation_number is None
for _ in range(10):
ga()
assert ga.fittest_individual == ['A', 'B', 'C', 'D', 'E']
assert ga.generation_number == 9
ga.reset()
assert ga.fittest_individual is None
assert ga.generation_number is None
def test_GeneticAlgorithm_02():
random.seed(87124)
ga = auxjad.GeneticAlgorithm(
target=['A', 'B', 'C', 'D', 'E'],
genes=['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],
)
assert ga.generation_number is None
ga()
assert ga.generation_number == 0
assert ga.fittest_individual == ['D', 'C', 'A', 'D', 'E']
ga()
assert ga.generation_number == 1
assert ga.fittest_individual == ['A', 'C', 'A', 'D', 'E']
ga()
assert ga.generation_number == 2
assert ga.fittest_individual == ['A', 'C', 'C', 'D', 'E']
def test_GeneticAlgorithm_03():
random.seed(55127)
ga = auxjad.GeneticAlgorithm(
target=['A', 'B', 'C', 'D', 'E'],
genes=['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],
population_size=4,
select_n_parents=2,
)
ga()
assert ga.population == [
['A', 'J', 'J', 'B', 'H'],
['D', 'A', 'E', 'A', 'F'],
['F', 'F', 'A', 'F', 'F'],
['F', 'F', 'E', 'J', 'C'],
]
assert ga.scores == [
0.209603072,
0.0912,
0.05638400000000001,
0.016396800000000003,
]
def test_GeneticAlgorithm_06():
random.seed(51244)
ga = auxjad.GeneticAlgorithm(
target=['A', 'B', 'C', 'D', 'E'],
genes=['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],
initial_individual=['F', 'G', 'H', 'I', 'J'],
)
expected_results = [
['F', 'G', 'H', 'I', 'J'],
['F', 'B', 'H', 'I', 'J'],
['F', 'B', 'H', 'I', 'E'],
['F', 'B', 'H', 'I', 'E'],
['A', 'B', 'H', 'I', 'E'],
['A', 'B', 'E', 'I', 'E'],
['A', 'B', 'E', 'D', 'E'],
['A', 'B', 'E', 'D', 'E'],
['A', 'B', 'E', 'D', 'E'],
['A', 'B', 'D', 'D', 'E'],
]
for expected_result in expected_results:
ga()
assert ga.fittest_individual == expected_result
def test_GeneticAlgorithm_04():
random.seed(60012)
ga = auxjad.GeneticAlgorithm(
target=['A', 'B', 'C', 'D', 'E'],
genes=['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],
population_size=50,
)
expected_results = [
['A', 'J', 'B', 'E', 'E'],
['A', 'H', 'C', 'D', 'J'],
['A', 'C', 'D', 'D', 'E'],
['A', 'C', 'D', 'D', 'E'],
['A', 'C', 'D', 'D', 'E'],
['A', 'C', 'C', 'D', 'E'],
['A', 'C', 'C', 'D', 'E'],
['A', 'C', 'C', 'D', 'E'],
['A', 'C', 'C', 'D', 'E'],
['A', 'C', 'C', 'D', 'E'],
]
expected_scores = [
0.480000512,
0.6000768,
0.6799999999999999,
0.6799999999999999,
0.6799999999999999,
0.8400000000000001,
0.8400000000000001,
0.8400000000000001,
0.8400000000000001,
0.8400000000000001,
]
for i in range(10):
ga()
assert ga.generation_number == i
assert ga.fittest_individual == expected_results[i]
assert ga.fittest_individual_score == expected_scores[i]
def test_GeneticAlgorithm_07():
random.seed(51244)
ga = auxjad.GeneticAlgorithm(
target=['A', 'B', 'C', 'D', 'E'],
genes=['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],
)
assert ga.population_size == 100
assert ga.select_n_parents == 10
assert ga.keep_n_parents == 0
assert ga.mutation_chance == 0.2
assert ga.mutation_index == 0.1
assert ga.evaluation_index == 0.2
ga.population_size = 50
ga.select_n_parents = 15
ga.keep_n_parents = 3
ga.mutation_chance = 0.75
ga.mutation_index = 0.25
ga.evaluation_index = 0.5
assert ga.population_size == 50
assert ga.select_n_parents == 15
assert ga.keep_n_parents == 3
assert ga.mutation_chance == 0.75
assert ga.mutation_index == 0.25
assert ga.evaluation_index == 0.5
