def gen_conf4():
param_values = deepcopy(default_param_values)
brkga_params = param_values["params"]
chromosome_size = 500
instance = Instance(chromosome_size)
brkga_params.population_size = 100
brkga_params.elite_percentage = 0.35
brkga_params.mutants_percentage = 0.15
brkga_params.num_elite_parents = 1
brkga_params.total_parents = 2
brkga_params.bias_type = BiasFunctionType.EXPONENTIAL
brkga_params.num_independent_populations = 3
brkga_params.pr_number_pairs = 0
brkga_params.pr_minimum_distance = 0.0
brkga_params.pr_type = PathRelinkingType.DIRECT
brkga_params.pr_selection = PathRelinkingSelection.BESTSOLUTION
brkga_params.alpha_block_size = 1.0
brkga_params.pr_percentage = 1.0
param_values["decoder"] = SumDecode(instance)
param_values["sense"] = Sense.MINIMIZE
param_values["seed"] = 2947804214761
param_values["chromosome_size"] = chromosome_size
param_values["evolutionary_mechanism_on"] = True
print("\n> Building configuration 4")
brkga = BrkgaMpIpr(**param_values)
brkga.initialize()
rho = 0.75
brkga.set_bias_custom_function(lambda x: rho if x == 1 else 1.0 - rho)
print("> Writing configuration 4")
with open(os.path.join(STATE_DIR, "state4.pickle"), "wb") as hd:
pickle.dump(brkga, hd)
print("> Evolving population 0...")
brkga.evolve_population(0)
fitness1 = brkga.get_best_fitness()
chromosome1 = brkga.get_best_chromosome()
print("> Evolving population 1...")
brkga.evolve_population(1)
fitness2 = brkga.get_best_fitness()
chromosome2 = brkga.get_best_chromosome()
print("> Evolving population 2...")
brkga.evolve_population(2)
fitness3 = brkga.get_best_fitness()
chromosome3 = brkga.get_best_chromosome()
print("> Evolving both populations for 100 generations...")
for _ in range(100):
start_time = time()
brkga.evolve_population(0)
brkga.evolve_population(1)
brkga.evolve_population(2)
print(f"Elapsed time: {time() - start_time :.2f}")
fitness103 = brkga.get_best_fitness()
chromosome103 = brkga.get_best_chromosome()
with open(os.path.join(SOLUTION_DIR, "best_solution4.pickle"), "wb") as hd:
pickle.dump(
{
"fitness1": fitness1,
"chromosome1": chromosome1,
"fitness2": fitness2,
"chromosome2": chromosome2,
"fitness3": fitness2,
"chromosome3": chromosome2,
"fitness103": fitness103,
"chromosome103": chromosome103,
},
hd
)
def test_set_bias_custom_function(self):
"""
Tests set_bias_custom_function() method.
"""
param_values = deepcopy(self.default_param_values)
param_values["params"].population_size = 100
param_values["params"].total_parents = 10
brkga = BrkgaMpIpr(**param_values)
# After build, brkga_params function is never CUSTOM
self.assertNotEqual(brkga.params.bias_type, BiasFunctionType.CUSTOM)
with self.assertRaises(ValueError) as context:
brkga.set_bias_custom_function(lambda x: -x)
self.assertEqual(
str(context.exception).strip(),
"Bias function must be positive non-increasing")
with self.assertRaises(ValueError) as context:
brkga.set_bias_custom_function(lambda x: x)
self.assertEqual(
str(context.exception).strip(),
"Bias function is not a non-increasing function")
with self.assertRaises(ValueError) as context:
brkga.set_bias_custom_function(lambda x: x + 1)
self.assertEqual(
str(context.exception).strip(),
"Bias function is not a non-increasing function")
with self.assertRaises(ValueError) as context:
brkga.set_bias_custom_function(lambda x: math.log1p(x))
self.assertEqual(
str(context.exception).strip(),
"Bias function is not a non-increasing function")
brkga.set_bias_custom_function(lambda x: 1.0 / math.log1p(x))
self.assertAlmostEqual(brkga._total_bias_weight, 6.554970525044798)
# After 2nd call to set_bias_custom_function, brkga_params function
# is always CUSTOM
self.assertEqual(brkga.params.bias_type, BiasFunctionType.CUSTOM)
brkga.set_bias_custom_function(lambda x: 1.0 / x)
self.assertAlmostEqual(brkga._total_bias_weight, 2.9289682539682538)
self.assertEqual(brkga.params.bias_type, BiasFunctionType.CUSTOM)
brkga.set_bias_custom_function(lambda x: x**-2.0)
self.assertAlmostEqual(brkga._total_bias_weight, 1.5497677311665408)
self.assertEqual(brkga.params.bias_type, BiasFunctionType.CUSTOM)
brkga.set_bias_custom_function(lambda x: x**-3.0)
self.assertAlmostEqual(brkga._total_bias_weight, 1.197531985674193)
self.assertEqual(brkga.params.bias_type, BiasFunctionType.CUSTOM)
brkga.set_bias_custom_function(lambda x: math.exp(-x))
self.assertAlmostEqual(brkga._total_bias_weight, 0.5819502851677112)
self.assertEqual(brkga.params.bias_type, BiasFunctionType.CUSTOM)
# This is a constance function.
brkga.set_bias_custom_function(
lambda _: 1.0 / brkga.params.total_parents)
self.assertAlmostEqual(brkga._total_bias_weight, 0.9999999999999999)
self.assertEqual(brkga.params.bias_type, BiasFunctionType.CUSTOM)
brkga.set_bias_custom_function(lambda x: 0.6325 / math.sqrt(x))
self.assertAlmostEqual(brkga._total_bias_weight, 3.175781171302612)
self.assertEqual(brkga.params.bias_type, BiasFunctionType.CUSTOM)
#############################################
# Constant functions test for standard BRKGA
#############################################
param_values = deepcopy(self.default_param_values)
param_values["params"].num_elite_parents = 1
param_values["params"].total_parents = 2
brkga = BrkgaMpIpr(**param_values)
rho = 0.5
brkga.set_bias_custom_function(lambda x: rho if x == 1 else 1.0 - rho)
self.assertAlmostEqual(brkga._total_bias_weight, 1.0)
self.assertAlmostEqual(brkga._bias_function(1), 0.5)
self.assertAlmostEqual(brkga._bias_function(2), 0.5)
rho = 0.75
brkga.set_bias_custom_function(lambda x: rho if x == 1 else 1.0 - rho)
self.assertAlmostEqual(brkga._total_bias_weight, 1.0)
self.assertAlmostEqual(brkga._bias_function(1), 0.75)
self.assertAlmostEqual(brkga._bias_function(2), 0.25)
rho = 0.9
brkga.set_bias_custom_function(lambda x: rho if x == 1 else 1.0 - rho)
self.assertAlmostEqual(brkga._total_bias_weight, 1.0)
self.assertAlmostEqual(brkga._bias_function(1), 0.9)
self.assertAlmostEqual(brkga._bias_function(2), 0.1)
