def test_can_set_initial_population(
default_evolutionary_algorithm: EvolutionaryAlgorithm, ):
ind1 = default_evolutionary_algorithm.individual_structures.build()
ind2 = default_evolutionary_algorithm.individual_structures.build()
default_evolutionary_algorithm.set_initial_population([ind1, ind2])
assert ind1 in default_evolutionary_algorithm._population
assert ind2 in default_evolutionary_algorithm._population
def test_initial_population_is_preserved(
default_evolutionary_algorithm: EvolutionaryAlgorithm, ):
ind1 = default_evolutionary_algorithm.individual_structures.build()
ind2 = default_evolutionary_algorithm.individual_structures.build()
default_evolutionary_algorithm.set_initial_population([ind1, ind2])
assert ind1 in default_evolutionary_algorithm._population
assert ind2 in default_evolutionary_algorithm._population
default_evolutionary_algorithm.ensure_population_initialized()
assert ind1 in default_evolutionary_algorithm._population
assert ind2 in default_evolutionary_algorithm._population
assert (len(default_evolutionary_algorithm._population) ==
default_evolutionary_algorithm.population_size)
def test_iteration_regenerates_right_sized_population():
eva = EvolutionaryAlgorithm(
ranker=Ranker(lambda x: 1.0),
selector=Random(selection_size=1),
population_size=5,
generations=1,
individual_structure=UniformIndividualStructure(
IntGene(lower_bound=0, upper_bound=1)),
multiple_individual_operators=[],
single_individual_operators=[],
)
eva.run()
assert len(eva._population) == 5
def test_iteration_calls_selector():
selector = Random(selection_size=1)
eva = EvolutionaryAlgorithm(
ranker=Ranker(lambda x: 1.0),
selector=selector,
population_size=1,
generations=1,
individual_structure=UniformIndividualStructure(
IntGene(lower_bound=0, upper_bound=1)),
multiple_individual_operators=[],
single_individual_operators=[],
)
expect(selector).__call__.once().and_return([(1, )])
eva.run()
assert eva._population[0] == (1, )
def test_iteration_calls_ranker():
ranker = Ranker(lambda x: 1.0)
assert not ranker.ranked_population
eva = EvolutionaryAlgorithm(
ranker=ranker,
selector=Random(selection_size=1),
population_size=1,
generations=1,
individual_structure=UniformIndividualStructure(
IntGene(lower_bound=0, upper_bound=1)),
multiple_individual_operators=[],
single_individual_operators=[],
)
eva.run()
assert len(eva._population) == len(ranker.ranked_population)
assert set(eva._population) == set(r[0] for r in ranker.ranked_population)
def test_iteration_calls_multiple_individual_operator():
individual_structure = UniformIndividualStructure(
IntGene(lower_bound=0, upper_bound=1))
crossover_operator = OnePointCrossoverOperator(individual_structure)
eva = EvolutionaryAlgorithm(
ranker=Ranker(lambda x: 1.0),
selector=Random(selection_size=1),
population_size=1,
generations=1,
individual_structure=individual_structure,
multiple_individual_operators=[(crossover_operator, 1)],
single_individual_operators=[],
)
expect(crossover_operator).__call__.once().and_return((1, ))
eva.run()
assert eva._population[0] == (1, )
def test_iterations_calls_callback():
counter = 0
def callback(_: Evolution):
nonlocal counter
counter += 1
eva = EvolutionaryAlgorithm(
ranker=Ranker(lambda x: 1.0),
selector=Random(selection_size=1),
population_size=1,
generations=1,
individual_structure=UniformIndividualStructure(
IntGene(lower_bound=0, upper_bound=1)),
multiple_individual_operators=[],
single_individual_operators=[],
iteration_callbacks=[callback],
)
eva.run()
assert counter == 1
def test_iteration_calls_multiple_instances_of_single_individual_operator():
individual_structure = UniformIndividualStructure(
IntGene(lower_bound=0, upper_bound=1))
mutation_operator = MutationOperator(individual_structure)
mutation_operator_2 = MutationOperator(individual_structure)
eva = EvolutionaryAlgorithm(
ranker=Ranker(lambda x: 1.0),
selector=Random(selection_size=1),
population_size=1,
generations=1,
individual_structure=individual_structure,
multiple_individual_operators=[],
single_individual_operators=[(mutation_operator, 1),
(mutation_operator_2, 1)],
)
expect(mutation_operator).__call__.once().and_return((1, ))
expect(mutation_operator_2).__call__.once().with_args((1, )).and_return(
(0, ))
eva.run()
assert eva._population[0] == (0, )
def default_evolutionary_algorithm(
default_score_function) -> EvolutionaryAlgorithm:
return EvolutionaryAlgorithm(
population_size=10,
generations=10,
selector=Tournament(selection_size=2, tournament_size=10),
ranker=Ranker(default_score_function),
individual_structure=UniformIndividualStructure(
tuple(IntGene(lower_bound=0, upper_bound=10) for _ in range(10))),
multiple_individual_operators=[],
single_individual_operators=[],
)
def initialize(self, score_function: Callable[[IndividualType], Q]):
if self._selector:
return EvolutionaryAlgorithm(
population_size=self.population_size,
generations=self.generations,
individual_structure=self._individual_structure,
ranker=self._ranker(score_function, executor=self._executor),
selector=self._selector,
multiple_individual_operators=self._multiple_individual_operators,
single_individual_operators=self._single_individual_operators,
elite_size=self.elite_size,
iteration_callbacks=self._callbacks,
)
assert False, "Must specify selector!"
def test_iteration_preserves_elites():
ranker = Ranker(lambda x: 1.0)
expect(ranker).rank.once()
ranker.ranked_population = [((0, ), 3)] + [((1, ), 0)] * 4
selector = Random(selection_size=1)
expect(selector).__call__.once().and_return([(1, )])
eva = EvolutionaryAlgorithm(
ranker=ranker,
selector=selector,
population_size=5,
generations=1,
individual_structure=UniformIndividualStructure(
IntGene(lower_bound=0, upper_bound=1)),
multiple_individual_operators=[],
single_individual_operators=[],
elite_size=1,
)
# invert order, to make sure that we're indeed selecting the elites.
eva.set_initial_population(ranker.ranked_population[-1:])
eva.run()
assert eva._population[0] == (0, )
assert len(eva._population) == 5
def test_ensure_run_initialises_population(
default_evolutionary_algorithm: EvolutionaryAlgorithm, ):
default_evolutionary_algorithm.run()
assert (len(default_evolutionary_algorithm._population) ==
default_evolutionary_algorithm.population_size)
def test_ensure_initial_population_generates_right_number_of_individuals(
default_evolutionary_algorithm: EvolutionaryAlgorithm, ):
default_evolutionary_algorithm.ensure_population_initialized()
assert (len(default_evolutionary_algorithm._population) ==
default_evolutionary_algorithm.population_size)