def _runExperiments():
for ndim in Config.experiment_dims:
for fid in Config.experiment_funcs:
# Initialize the first individual in the population
discrete_part = [np.random.randint(len(x[1])) for x in options]
lamb = int(4 + np.floor(3 * np.log(parameters.n)))
int_part = [lamb]
float_part = [
parameters.mu, parameters.alpha_mu, parameters.c_sigma,
parameters.damps, parameters.c_c, parameters.c_1,
parameters.c_mu, 0.2, 0.955, 0.5, 0, 0.3, 0.5, 2
]
population = [
MixedIntIndividual(len(discrete_part) + len(int_part) +
len(float_part),
num_discrete=len(num_options_per_module),
num_ints=len(int_part))
]
population[0].genotype = np.array(discrete_part + int_part +
float_part)
population[0].fitness = ESFitness()
while len(population) < Config.GA_mu:
population.append(copy(population[0]))
parameters = Parameters(len(options) + 15,
Config.GA_budget,
mu=Config.GA_mu,
lambda_=Config.GA_lambda)
parameters.l_bound[len(options):] = np.array(
[2, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]).reshape(15)
parameters.u_bound[len(options):] = np.array(
[200, 1, 5, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5]).reshape(15)
u_bound, l_bound = create_bounds(float_part, 0.3)
parameters.u_bound[len(options) + 1:] = np.array(u_bound)
parameters.l_bound[len(options) + 1:] = np.array(l_bound)
print("Optimizing for function ID {} in {}-dimensional space:".
format(fid, ndim))
x = datetime.now()
gen_sizes, sigmas, fitness, best = _MIES(n=ndim,
fitnessFunction=fid,
budget=Config.GA_budget,
mu=Config.GA_mu,
lambda_=Config.GA_lambda,
parameters=parameters,
population=population)
y = datetime.now()
z = y - x
np.savez("{}final_GA_results_{}dim_f{}".format(
non_bbob_datapath, ndim, fid),
sigma=sigmas,
best_fitness=fitness,
best_result=best.genotype,
generation_sizes=gen_sizes,
time_spent=z)
def test_incorrect_percentage(self):
with self.assertRaises(ValueError):
create_bounds(self.values, percentage=3.0)
def test_default_behavior(self):
u_bound, l_bound = create_bounds(self.values, percentage=0.3)
np.testing.assert_array_almost_equal(
u_bound, [1, 1.3, 2.6, 3.9, 5.2, 6.5, 7.8, 9.1, 10.4, 11.7])
np.testing.assert_array_almost_equal(
l_bound, [0, 0.7, 1.4, 2.1, 2.8, 3.5, 4.2, 4.9, 5.6, 6.3])
def _runGA(ndim=5, fid=1, run=1):
x = datetime.now()
# Where to store genotype-fitness information
# storage_file = '{}GA_results_{}dim_f{}.tdat'.format(non_bbob_datapath, ndim, fid)
storage_file = '{}MIES_results_{}dim_f{}run_{}.tdat'.format(
non_bbob_datapath, ndim, fid, run)
# Fitness function to be passed on to the baseAlgorithm
fitnessFunction = partial(evaluateCustomizedESs,
fid=fid,
ndim=ndim,
iids=range(Config.ES_num_runs),
storage_file=storage_file)
parameters = Parameters(len(options) + 15,
Config.GA_budget,
mu=Config.GA_mu,
lambda_=Config.GA_lambda)
parameters.l_bound[len(options):] = np.array(
[2, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]).reshape(15)
parameters.u_bound[len(options):] = np.array(
[200, 1, 5, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5]).reshape(15)
# Initialize the first individual in the population
discrete_part = [np.random.randint(len(x[1])) for x in options]
lamb = int(4 + np.floor(3 * np.log(parameters.n)))
int_part = [lamb]
float_part = [
parameters.mu, parameters.alpha_mu, parameters.c_sigma,
parameters.damps, parameters.c_c, parameters.c_1, parameters.c_mu, 0.2,
0.955, 0.5, 0, 0.3, 0.5, 2
]
population = [
MixedIntIndividual(len(discrete_part) + len(int_part) +
len(float_part),
num_discrete=len(num_options_per_module),
num_ints=len(int_part))
]
population[0].genotype = np.array(discrete_part + int_part + float_part)
population[0].fitness = ESFitness()
while len(population) < Config.GA_mu:
population.append(copy(population[0]))
u_bound, l_bound = create_bounds(float_part, 0.3)
parameters.u_bound[len(options) + 1:] = np.array(u_bound)
parameters.l_bound[len(options) + 1:] = np.array(l_bound)
gen_sizes, sigmas, fitness, best = _MIES(
n=ndim,
fitnessFunction=fitnessFunction,
budget=Config.GA_budget,
mu=Config.GA_mu,
lambda_=Config.GA_lambda,
parameters=parameters,
population=population) # This line does all the work!
y = datetime.now()
print()
print("Best Individual: {}\n"
" Fitness: {}\n"
"Fitnesses over time: {}".format(best.genotype, best.fitness,
fitness))
z = _displayDuration(x, y)
if Config.write_output:
np.savez("{}final_GA_results_{}dim_f{}_run{}".format(
non_bbob_datapath, ndim, fid, run),
sigma=sigmas,
best_fitness=fitness,
best_result=best.genotype,
generation_sizes=gen_sizes,
time_spent=z)