def execute_generational_steps():
x = init_x_vals(-10, 10, 100)
y = equation_eval(x)
training_data = ExplicitTrainingData(x, y)
component_generator = ComponentGenerator(x.shape[1])
component_generator.add_operator(2)
component_generator.add_operator(3)
component_generator.add_operator(4)
crossover = AGraphCrossover(component_generator)
mutation = AGraphMutation(component_generator)
agraph_generator = AGraphGenerator(STACK_SIZE, component_generator)
fitness = ExplicitRegression(training_data=training_data)
local_opt_fitness = ContinuousLocalOptimization(fitness, algorithm='lm')
evaluator = Evaluation(local_opt_fitness)
ea = AgeFitnessEA(evaluator, agraph_generator, crossover,
mutation, 0.4, 0.4, POP_SIZE)
island = Island(ea, agraph_generator, POP_SIZE)
archipelago = SerialArchipelago(island)
opt_result = archipelago.evolve_until_convergence(max_generations=500,
fitness_threshold=1.0e-4)
if opt_result.success:
print(archipelago.get_best_individual().get_latex_string())
else:
print("Failed.")
def test_potential_hof_members(mocker, one_island):
island_a = mocker.Mock(hall_of_fame=['a'])
island_b = mocker.Mock(hall_of_fame=['b'])
island_c = mocker.Mock(hall_of_fame=['c'])
archipelago = SerialArchipelago(one_island, num_islands=3)
archipelago._islands = [island_a, island_b, island_c]
assert archipelago._get_potential_hof_members() == ['a', 'b', 'c']
def test_best_individual_returned(one_island):
archipelago = SerialArchipelago(one_island)
generator = MultipleValueChromosomeGenerator(generate_zero,
VALUE_LIST_SIZE)
best_indv = generator()
archipelago._islands[0].load_population([best_indv], replace=False)
assert archipelago.get_best_individual().fitness == 0
def test_archipelago_runs(one_island, two_island, three_island):
max_generations = 100
min_generations = 20
error_tol = 0
generation_step_report = 10
archipelago = SerialArchipelago(one_island, num_islands=4)
archipelago._islands = [one_island, two_island, three_island, three_island]
result = archipelago.evolve_until_convergence(max_generations, error_tol,
generation_step_report,
min_generations)
assert result.success
def test_island_migration(one_island, island_list):
archipelago = SerialArchipelago(one_island, num_islands=4)
archipelago._islands = island_list
archipelago._coordinate_migration_between_islands()
migration_count = 0
for i, island in enumerate(archipelago._islands):
initial_individual_values = [i] * VALUE_LIST_SIZE
for individual in island.population:
if initial_individual_values != individual.values:
migration_count += 1
break
assert len(island_list) == migration_count
def test_best_fitness_eval_count(one_island):
num_islands = 4
archipelago = SerialArchipelago(one_island, num_islands=num_islands)
assert archipelago.get_fitness_evaluation_count() == 0
archipelago.evolve(1)
expected_evaluations = num_islands * (POP_SIZE + OFFSPRING_SIZE)
assert archipelago.get_fitness_evaluation_count() == expected_evaluations
def test_archipelago_generated(island):
archipelago = SerialArchipelago(island, num_islands=3)
assert len(archipelago._islands) == 3
for island_i in archipelago._islands:
assert island_i != island
assert island_i._population_size == island._population_size
def test_total_population_not_affected_by_migration(one_island):
archipelago = SerialArchipelago(one_island, num_islands=4)
total_pop_before = sum([len(i.population) for i in archipelago._islands])
archipelago._coordinate_migration_between_islands()
total_pop_after = sum([len(i.population) for i in archipelago._islands])
assert total_pop_after == total_pop_before
def test_convergence_of_archipelago_unconverged(one_island):
archipelago = SerialArchipelago(one_island, num_islands=6)
assert archipelago.get_best_fitness() > 0
def test_convergence_of_archipelago(one_island, island_list):
archipelago = SerialArchipelago(one_island, num_islands=4)
archipelago._islands = island_list
assert archipelago.get_best_fitness() <= 0