def test_crossentropy_sphere():
optimizer = crossentropy.CrossEntropy(32, population_size=20)
optimizer.optimize(problems.sphere_binary,
max_iterations=1000,
logging_func=lambda *args: optimize._print_fitnesses(
*args, frequency=100))
assert optimizer.solution_found
def test_gsa_sphere():
optimizer = GSA(2, [-5.0] * 2, [5.0] * 2)
optimizer.optimize(problems.sphere_real,
max_iterations=1000,
logging_func=lambda *args: optimize._print_fitnesses(
*args, frequency=100))
assert optimizer.solution_found
def test_genalg_problems():
# Attempt to solve various problems
# Assert that the optimizer can find the solutions
optimizer = genalg.GenAlg(examplefunctions.ackley, 32,
decode_func=examplefunctions.ackley_binary)
optimizer._logging_func = lambda x, y, z : optimize._print_fitnesses(x, y, z, frequency=100)
optimizer.optimize()
assert optimizer.solution_found
def test_genalg_problems():
# Attempt to solve various problems
# Assert that the optimizer can find the solutions
optimizer = GenAlg(32)
optimizer.optimize(problems.ackley_binary,
logging_func=lambda *args: optimize._print_fitnesses(
*args, frequency=100))
assert optimizer.solution_found
def test_crossentropy_sphere():
optimizer = crossentropy.CrossEntropy(32, population_size=20)
optimizer.optimize(
problems.sphere_binary,
max_iterations=1000,
logging_func=
lambda *args: optimize._print_fitnesses(*args, frequency=100))
assert optimizer.solution_found
def test_gsa_sphere():
optimizer = GSA(2, [-5.0] * 2, [5.0] * 2)
optimizer.optimize(
problems.sphere_real,
max_iterations=1000,
logging_func=
lambda *args: optimize._print_fitnesses(*args, frequency=100))
assert optimizer.solution_found
def test_gsa_sphere():
optimizer = gsa.GSA(examplefunctions.sphere,
2, [-5.0] * 2, [5.0] * 2,
max_iterations=1000,
decode_func=examplefunctions.decode_real)
optimizer._logging_func = lambda x, y, z: optimize._print_fitnesses(
x, y, z, frequency=100)
optimizer.optimize()
assert optimizer.solution_found
def test_gsa_problems():
# Attempt to solve various problems
# Assert that the optimizer can find the solutions
optimizer = GSA(2, [-5.0] * 2, [5.0] * 2)
optimizer.optimize(problems.ackley_real,
max_iterations=1000,
logging_func=lambda *args: optimize._print_fitnesses(
*args, frequency=100))
assert optimizer.solution_found
def test_genalg_problems():
# Attempt to solve various problems
# Assert that the optimizer can find the solutions
optimizer = GenAlg(32)
optimizer.optimize(
problems.ackley_binary,
logging_func=
lambda *args: optimize._print_fitnesses(*args, frequency=100))
assert optimizer.solution_found
def test_gsa_problems():
# Attempt to solve various problems
# Assert that the optimizer can find the solutions
optimizer = GSA(2, [-5.0] * 2, [5.0] * 2)
optimizer.optimize(
problems.ackley_real,
max_iterations=1000,
logging_func=
lambda *args: optimize._print_fitnesses(*args, frequency=100))
assert optimizer.solution_found
def test_crossentropy_problems():
# Attempt to solve various problems
# Assert that the optimizer can find the solutions
# NOTE: since crossentropy is not very effective, we give it simpler problems
optimizer = crossentropy.CrossEntropy(32, population_size=20)
optimizer.optimize(problems.sphere_binary,
max_iterations=1000,
logging_func=lambda *args: optimize._print_fitnesses(
*args, frequency=100))
assert optimizer.solution_found
def test_gsa_problems():
# Attempt to solve various problems
# Assert that the optimizer can find the solutions
optimizer = gsa.GSA(examplefunctions.ackley,
2, [-5.0] * 2, [5.0] * 2,
max_iterations=1000,
decode_func=examplefunctions.decode_real)
optimizer._logging_func = lambda x, y, z: optimize._print_fitnesses(
x, y, z, frequency=100)
optimizer.optimize()
assert optimizer.solution_found
def test_crossentropy_problems():
# Attempt to solve various problems
# Assert that the optimizer can find the solutions
# NOTE: since crossentropy is not very effective, we give it simpler problems
optimizer = crossentropy.CrossEntropy(32, population_size=20)
optimizer.optimize(
problems.sphere_binary,
max_iterations=1000,
logging_func=
lambda *args: optimize._print_fitnesses(*args, frequency=100))
assert optimizer.solution_found
def test_crossentropy_sphere():
optimizer = crossentropy.CrossEntropy(
examplefunctions.sphere,
32,
population_size=20,
max_iterations=1000,
decode_func=examplefunctions.ackley_binary)
optimizer._logging_func = lambda x, y, z: optimize._print_fitnesses(
x, y, z, frequency=100)
optimizer.optimize()
assert optimizer.solution_found
def test_crossentropy_problems():
# Attempt to solve various problems
# Assert that the optimizer can find the solutions
# NOTE: since crossentropy is not very effective, we give it simpler problems
optimizer = crossentropy.CrossEntropy(
examplefunctions.sphere,
32,
population_size=20,
max_iterations=1000,
decode_func=examplefunctions.ackley_binary)
optimizer._logging_func = lambda x, y, z: optimize._print_fitnesses(
x, y, z, frequency=100)
optimizer.optimize()
print(1.0 / optimizer.best_fitness)
assert optimizer.solution_found
def test_metaoptimize_genalg():
optimizer = genalg.GenAlg(examplefunctions.ackley, 32,
decode_func=examplefunctions.ackley_binary)
optimizer._logging_func = lambda x, y, z : optimize._print_fitnesses(x, y, z, frequency=100)
prev_hyperparameters = optimizer._get_hyperparameters()
# Test without metaoptimize, save iterations to solution
optimizer.optimize()
iterations_to_solution = optimizer.iteration
# Test with metaoptimize, assert that iterations to solution is lower
optimizer.optimize_hyperparameters(smoothing=1, _meta_optimizer=genalg.GenAlg(None, None, 1, 1))
optimizer.optimize()
assert optimizer._get_hyperparameters() != prev_hyperparameters
#assert optimizer.iteration < iterations_to_solution # Improvements are made
def test_metaoptimize_gsa():
optimizer = gsa.GSA(examplefunctions.ackley,
2, [-5.0] * 2, [5.0] * 2,
max_iterations=1000,
decode_func=examplefunctions.decode_real)
optimizer._logging_func = lambda x, y, z: optimize._print_fitnesses(
x, y, z, frequency=100)
prev_hyperparameters = optimizer._get_hyperparameters()
# Test without metaoptimize, save iterations to solution
optimizer.optimize()
iterations_to_solution = optimizer.iteration
# Test with metaoptimize, assert that iterations to solution is lower
optimizer.optimize_hyperparameters(smoothing=1,
_meta_optimizer=genalg.GenAlg(
None, None, 1, 1))
optimizer.optimize()
assert optimizer._get_hyperparameters() != prev_hyperparameters
def test_genalg_sphere():
optimizer = genalg.GenAlg(examplefunctions.sphere, 32,
decode_func=examplefunctions.ackley_binary)
optimizer._logging_func = lambda x, y, z : optimize._print_fitnesses(x, y, z, frequency=100)
optimizer.optimize()
assert optimizer.solution_found
def _check_optimizer(optimizer):
optimizer.optimize(problems.sphere_binary,
logging_func=lambda *args: optimize._print_fitnesses(
*args, frequency=100))
assert optimizer.solution_found
def _check_optimizer(optimizer):
optimizer.optimize(
problems.sphere_binary,
logging_func=
lambda *args: optimize._print_fitnesses(*args, frequency=100))
assert optimizer.solution_found
