def training_function(training_data, ea_choice):
component_generator = \
ComponentGenerator(input_x_dimension=training_data.x.shape[1])
component_generator.add_operator("+")
component_generator.add_operator("-")
component_generator.add_operator("*")
agraph_generator = AGraphGenerator(agraph_size=32,
component_generator=component_generator)
crossover = AGraphCrossover(component_generator)
mutation = AGraphMutation(component_generator)
fitness = ExplicitRegression(training_data=training_data)
local_opt_fitness = ContinuousLocalOptimization(fitness, algorithm='lm')
evaluator = Evaluation(local_opt_fitness)
POPULATION_SIZE = 64
MUTATION_PROBABILITY = 0.1
CROSSOVER_PROBABILITY = 0.7
if ea_choice == "age_fitness":
ea = AgeFitnessEA(evaluator, agraph_generator, crossover, mutation,
MUTATION_PROBABILITY, CROSSOVER_PROBABILITY,
POPULATION_SIZE)
else:
ea = DeterministicCrowdingEA(evaluator, crossover, mutation,
MUTATION_PROBABILITY,
CROSSOVER_PROBABILITY)
island = Island(ea, agraph_generator, POPULATION_SIZE)
opt_result = island.evolve_until_convergence(
max_generations=MAX_GENERATIONS, fitness_threshold=1e-6)
return island.get_best_individual(), opt_result
def run_one_max_problem():
generator = create_chromosome_generator()
ev_alg = create_evolutionary_algorithm()
island = Island(ev_alg, generator, population_size=10)
display_best_individual(island)
island.evolve(num_generations=50)
display_best_individual(island)
def init_island():
np.random.seed(10)
x = init_x_vals(START, STOP, NUM_POINTS)
y = equation_eval(x)
training_data = ExplicitTrainingData(x, y)
component_generator = ComponentGenerator(
x.shape[1],
automatic_constant_optimization=False,
numerical_constant_range=10)
component_generator.add_operator("+")
component_generator.add_operator("-")
component_generator.add_operator("*")
crossover = AGraphCrossover(component_generator)
mutation = AGraphMutation(component_generator)
agraph_generator = AGraphGenerator(STACK_SIZE, component_generator)
fitness = ExplicitRegression(training_data=training_data)
evaluator = Evaluation(fitness)
ea = AgeFitnessEA(evaluator, agraph_generator, crossover, mutation,
MUTATION_PROBABILITY, CROSSOVER_PROBABILITY, POP_SIZE)
island = Island(ea, agraph_generator, POP_SIZE)
return island
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 init_island():
np.random.seed(15)
x = init_x_vals(START, STOP, NUM_POINTS)
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_algorithm = AgeFitnessEA(evaluator, agraph_generator, crossover,
mutation, MUTATION_PROBABILITY,
CROSSOVER_PROBABILITY, POP_SIZE)
island = Island(ea_algorithm, agraph_generator, POP_SIZE)
return island
def test_island_hof(mocker):
hof = mocker.Mock()
crossover = SinglePointCrossover()
mutation = SinglePointMutation(mutation_function)
selection = Tournament(10)
fitness = MultipleValueFitnessFunction()
evaluator = Evaluation(fitness)
ev_alg = MuPlusLambda(evaluator, selection, crossover, mutation,
0.2, 0.4, 20)
generator = MultipleValueChromosomeGenerator(mutation_function, 10)
island = Island(ev_alg, generator, 25, hall_of_fame=hof)
island.evolve(10)
hof.update.assert_called_once()
hof_update_pop = hof.update.call_args[0][0]
for h, i in zip(hof_update_pop, island.population):
assert h == i
def island():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(mutation_function)
selection = Tournament(10)
fitness = MultipleValueFitnessFunction()
evaluator = Evaluation(fitness)
ev_alg = MuPlusLambda(evaluator, selection, crossover, mutation,
0.2, 0.4, 20)
generator = MultipleValueChromosomeGenerator(mutation_function, 10)
return Island(ev_alg, generator, 25)
def execute_generational_steps():
communicator = MPI.COMM_WORLD
rank = MPI.COMM_WORLD.Get_rank()
data = pd.read_csv('./data/fp_2var_test9_py.csv').as_matrix()
x = data[:, 0:2]
y = data[:2]
if rank == 0:
x = init_x_vals(-10, 10, 100)
y = equation_eval(x)
x = MPI.COMM_WORLD.bcast(x, root=0)
y = MPI.COMM_WORLD.bcast(y, root=0)
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)
component_generator.add_operator(5)
component_generator.add_operator(6)
component_generator.add_operator(7)
component_generator.add_operator(10)
crossover = AGraphCrossover(component_generator)
mutation = AGraphMutation(component_generator)
agraph_generator = AGraphGenerator(STACK_SIZE, component_generator)
fitness = ExplicitRegression(training_data=training_data,
metric='root mean squared error')
local_opt_fitness = ContinuousLocalOptimization(fitness,
algorithm='L-BFGS-B')
evaluator = Evaluation(local_opt_fitness)
#ea = AgeFitnessEA(evaluator, agraph_generator, crossover,
# mutation, 0.4, 0.4, POP_SIZE)
ea = DeterministicCrowdingEA(evaluator, crossover, mutation, 0.4, 0.4)
island = Island(ea, agraph_generator, POP_SIZE)
archipelago = ParallelArchipelago(island)
opt_result = archipelago.evolve_until_convergence(
MAX_GENERATIONS,
fitness_threshold=FITNESS_THRESHOLD,
min_generations=MIN_GENERATIONS,
stagnation_generations=STAGNATION_LIMIT)
if opt_result.success:
if rank == 0:
print("best: ", archipelago.get_best_individual())
def explicit_regression_benchmark():
np.random.seed(15)
communicator = MPI.COMM_WORLD
rank = MPI.COMM_WORLD.Get_rank()
x = None
y = None
if rank == 0:
x = init_x_vals(-10, 10, 100)
y = equation_eval(x)
x = MPI.COMM_WORLD.bcast(x, root=0)
y = MPI.COMM_WORLD.bcast(y, root=0)
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 = ParallelArchipelago(island)
opt_result = archipelago.evolve_until_convergence(max_generations=500,
fitness_threshold=1.0e-4)
if rank == 0:
if opt_result.success:
print("print the best indv", archipelago.get_best_individual())
else:
print("Failed.")
def main():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(get_random_float)
selection = Tournament(10)
fitness = ZeroMinFitnessFunction()
local_opt_fitness = ContinuousLocalOptimization(fitness)
evaluator = Evaluation(local_opt_fitness)
ea = MuPlusLambda(evaluator, selection, crossover, mutation, 0.4, 0.4, 20)
generator = MultipleFloatChromosomeGenerator(get_random_float, 8)
island = Island(ea, generator, 25)
island.evolve(1)
report_max_min_mean_fitness(island)
island.evolve(500)
report_max_min_mean_fitness(island)
def num_island(num, pop_size=POP_SIZE):
generator = MultipleValueChromosomeGenerator(NumberGenerator(num),
VALUE_LIST_SIZE)
return Island(evol_alg(), generator, pop_size)
def three_island(evol_alg):
generator = MultipleValueChromosomeGenerator(generate_three,
VALUE_LIST_SIZE)
return Island(evol_alg, generator, POP_SIZE)
def island(evol_alg):
generator = MultipleValueChromosomeGenerator(mutation_function,
VALUE_LIST_SIZE)
return Island(evol_alg, generator, POP_SIZE)
