def test_should_the_solution_remain_unchanged_if_the_probability_is_zero(
self):
operator = PolynomialMutation(0.0)
solution = FloatSolution(2, 1, [-5, -5, -5], [5, 5, 5])
solution.variables = [1.0, 2.0, 3.0]
mutated_solution = operator.execute(solution)
self.assertEqual([1.0, 2.0, 3.0], mutated_solution.variables)
def test_should_the_solution_change__if_the_probability_is_one(self):
operator = PolynomialMutation(1.0)
solution = FloatSolution([-5, -5, -5], [5, 5, 5], 2)
solution.variables = [1.0, 2.0, 3.0]
mutated_solution = operator.execute(solution)
self.assertNotEqual([1.0, 2.0, 3.0], mutated_solution.variables)
def test_should_execute_raise_and_exception_if_the_types_of_the_solutions_do_not_match_the_operators(
self):
operator = CompositeMutation(
[PolynomialMutation(1.0, 5.0),
PolynomialMutation(0.9, 25.0)])
float_solution = FloatSolution([2.0], [3.9], 3)
binary_solution = BinarySolution(1, 3, 0)
float_solution.variables = [3.0]
composite_solution = CompositeSolution(
[float_solution, binary_solution])
with self.assertRaises(InvalidConditionException):
operator.execute(composite_solution)
def test_should_constructor_create_a_valid_operator_when_adding_a_single_mutation_operator(
self):
mutation: Mutation = PolynomialMutation(0.9, 20.0)
operator = CompositeMutation([mutation])
self.assertIsNotNone(operator)
self.assertEqual(1, len(operator.mutation_operators_list))
def setUp(self) -> None:
problem1 = OneMax(number_of_bits=512)
self.emas1 = Emas(
problem=problem1,
initial_population_size=1000,
initial_inidividual_energy=10,
reproduction_threshold=20,
energy_exchange_operator=FractionEnergyExchange(0.5),
death_operator=ThresholdDeath(threshold=5, neighbours_operator=RandomNeighbours()),
termination_criterion=StoppingByEvaluations(max_evaluations=100000),
neighbours_operator=RandomNeighbours(),
reproduction_operator=FractionEnergyReproduction(0.5, BitFlipMutation(0.5), SPXCrossover(0.5))
)
problem2 = Sphere(number_of_variables=10)
self.emas2 = Emas(
problem=problem2,
initial_population_size=1000,
initial_inidividual_energy=10,
reproduction_threshold=20,
energy_exchange_operator=FractionEnergyExchange(0.5),
death_operator=ThresholdDeath(threshold=5, neighbours_operator=RandomNeighbours()),
termination_criterion=StoppingByEvaluations(max_evaluations=50000),
neighbours_operator=RandomNeighbours(),
reproduction_operator=FractionEnergyReproduction(0.5, PolynomialMutation(0.5), SBXCrossover(0.5))
)
def configurar_SMPSO(self):
algorithm = SMPSO(
problem=self.problema,
swarm_size=self.maxima_poblacion,
mutation=PolynomialMutation(probability=self.probabilidad, distribution_index=20),
leaders=CrowdingDistanceArchive(100),
termination_criterion=StoppingByEvaluations(max_evaluations=self.evaluaciones))
return algorithm
def test_should_execute_work_with_a_solution_subclass_of_float_solution(
self):
class NewFloatSolution(FloatSolution):
def __init__(self,
lower_bound: List[float],
upper_bound: List[float],
number_of_objectives: int,
number_of_constraints: int = 0):
super(NewFloatSolution,
self).__init__(lower_bound, upper_bound,
number_of_objectives,
number_of_constraints)
operator = PolynomialMutation(1.0)
solution = NewFloatSolution([-5, -5, -5], [5, 5, 5], 2)
solution.variables = [1.0, 2.0, 3.0]
mutated_solution = operator.execute(solution)
self.assertNotEqual([1.0, 2.0, 3.0], mutated_solution.variables)
def configurar_SPEA2(self):
algorithm = SPEA2(
problem=self.problema,
population_size=self.maxima_poblacion,
offspring_population_size=self.maxima_poblacion,
mutation=PolynomialMutation(probability=self.probabilidad, distribution_index=0.20),
crossover=SBXCrossover(probability=self.probabilidad, distribution_index=20),
termination_criterion=StoppingByEvaluations(max_evaluations=self.evaluaciones),
dominance_comparator=DominanceComparator())
return algorithm
def configurar_NSGAIII(self):
algorithm = NSGAIII(
problem=self.problema,
reference_directions = UniformReferenceDirectionFactory(5, n_points=91),
population_size=self.maxima_poblacion,
mutation=PolynomialMutation(probability = self.probabilidad , distribution_index=0.20),
crossover=SBXCrossover(probability= self.probabilidad, distribution_index=20),
termination_criterion=StoppingByEvaluations(max_evaluations=self.evaluaciones),
dominance_comparator=DominanceComparator())
return algorithm
def test_should_constructor_create_a_valid_operator_when_adding_two_mutation_operators(
self):
polynomial_mutation = PolynomialMutation(1.0, 20.0)
bit_flip_mutation = BitFlipMutation(0.01)
operator = CompositeMutation([polynomial_mutation, bit_flip_mutation])
self.assertIsNotNone(operator)
self.assertEqual(2, len(operator.mutation_operators_list))
self.assertTrue(
issubclass(operator.mutation_operators_list[0].__class__,
PolynomialMutation))
self.assertTrue(
issubclass(operator.mutation_operators_list[1].__class__,
BitFlipMutation))
client = Client('192.168.213.3:8786')
ncores = sum(client.ncores().values())
print(f'{ncores} cores available on cluster')
# creates the problem
problem = ZDT1()
# creates the algorithm
max_evaluations = 25000
algorithm = DistributedNSGAII(
problem=problem,
population_size=100,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
number_of_cores=ncores,
client=client)
algorithm.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
algorithm.observable.register(observer=VisualizerObserver())
algorithm.run()
front = algorithm.get_result()
print('Computing time: ' + str(algorithm.total_computing_time))
def test_should_constructor_raise_an_exception_if_the_probability_is_lower_than_zero(
self):
with self.assertRaises(Exception):
PolynomialMutation(-12)
def test_should_constructor_raise_an_exception_if_the_probability_is_greater_than_one(
self):
with self.assertRaises(Exception):
PolynomialMutation(2)
def test_should_constructor_create_a_valid_operator(self):
operator = PolynomialMutation(0.5, 20)
self.assertEqual(0.5, operator.probability)
self.assertEqual(20, operator.distribution_index)
def test_should_constructor_create_a_non_null_object(self):
mutation = PolynomialMutation(1.0)
self.assertIsNotNone(mutation)
def test_should_constructor_raises_an_exception_is_probability_is_higher_than_one(
self) -> None:
with self.assertRaises(Exception):
PolynomialMutation(1.01)
def test_should_constructor_raises_an_exception_is_probability_is_negative(
self) -> None:
with self.assertRaises(Exception):
PolynomialMutation(-1)
