def gen_conf1():
param_values = deepcopy(default_param_values)
brkga_params = param_values["params"]
brkga_params.population_size = 100
brkga_params.elite_percentage = 0.3
brkga_params.mutants_percentage = 0.1
brkga_params.num_elite_parents = 1
brkga_params.total_parents = 2
brkga_params.bias_type = BiasFunctionType.LOGINVERSE
brkga_params.num_independent_populations = 1
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"] = sum_decoder
param_values["sense"] = Sense.MAXIMIZE
param_values["seed"] = 4013750229
param_values["chromosome_size"] = chromosome_size
param_values["evolutionary_mechanism_on"] = True
print("\n> Building configuration 1")
brkga = BrkgaMpIpr(**param_values)
brkga.initialize()
print("> Writing configuration 1")
with open(os.path.join(STATE_DIR, "state1.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()
brkga.evolve_population(0)
fitness2 = brkga.get_best_fitness()
chromosome2 = brkga.get_best_chromosome()
for _ in range(100):
brkga.evolve_population(0)
fitness102 = brkga.get_best_fitness()
chromosome102 = brkga.get_best_chromosome()
with open(os.path.join(SOLUTION_DIR, "best_solution1.pickle"), "wb") as hd:
pickle.dump(
{
"fitness1": fitness1,
"chromosome1": chromosome1,
"fitness2": fitness2,
"chromosome2": chromosome2,
"fitness102": fitness102,
"chromosome102": chromosome102,
},
hd
)
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 gen_conf2():
param_values = deepcopy(default_param_values)
brkga_params = param_values["params"]
chromosome_size = 1000
instance = Instance(chromosome_size)
brkga_params.population_size = 500
brkga_params.elite_percentage = 0.25
brkga_params.mutants_percentage = 0.25
brkga_params.num_elite_parents = 5
brkga_params.total_parents = 50
brkga_params.bias_type = BiasFunctionType.QUADRATIC
brkga_params.num_independent_populations = 2
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"] = RankDecode(instance)
param_values["sense"] = Sense.MINIMIZE
param_values["seed"] = 1297832326904308
param_values["chromosome_size"] = chromosome_size
param_values["evolutionary_mechanism_on"] = True
print("\n> Building configuration 2")
brkga = BrkgaMpIpr(**param_values)
brkga.initialize()
print("> Writing configuration 2")
with open(os.path.join(STATE_DIR, "state2.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 both populations for 100 generations...")
for _ in range(100):
start_time = time()
brkga.evolve_population(0)
brkga.evolve_population(1)
print(f"Elapsed time: {time() - start_time :.2f}")
fitness102 = brkga.get_best_fitness()
chromosome102 = brkga.get_best_chromosome()
with open(os.path.join(SOLUTION_DIR, "best_solution2.pickle"), "wb") as hd:
pickle.dump(
{
"fitness1": fitness1,
"chromosome1": chromosome1,
"fitness2": fitness2,
"chromosome2": chromosome2,
"fitness102": fitness102,
"chromosome102": chromosome102,
},
hd
)
def gen_conf3():
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 = 0.17
brkga_params.num_elite_parents = 3
brkga_params.total_parents = 5
brkga_params.bias_type = BiasFunctionType.EXPONENTIAL
brkga_params.num_independent_populations = 5
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"] = 253624607406
param_values["chromosome_size"] = chromosome_size
param_values["evolutionary_mechanism_on"] = True
print("\n> Building configuration 3")
brkga = BrkgaMpIpr(**param_values)
brkga.initialize()
print("> Writing configuration 3")
with open(os.path.join(STATE_DIR, "state3.pickle"), "wb") as hd:
pickle.dump(brkga, hd)
print("> Evolving both populations for one generation...")
for i in range(brkga_params.num_independent_populations):
brkga.evolve_population(i)
fitness1 = brkga.get_best_fitness()
chromosome1 = brkga.get_best_chromosome()
print("> Evolving both populations for another generation...")
for i in range(brkga_params.num_independent_populations):
brkga.evolve_population(i)
brkga.evolve_population(1)
fitness2 = brkga.get_best_fitness()
chromosome2 = brkga.get_best_chromosome()
print("> Evolving both populations for 100 generations...")
for _ in range(100):
start_time = time()
for i in range(brkga_params.num_independent_populations):
brkga.evolve_population(i)
print(f"Elapsed time: {time() - start_time :.2f}")
fitness102 = brkga.get_best_fitness()
chromosome102 = brkga.get_best_chromosome()
with open(os.path.join(SOLUTION_DIR, "best_solution3.pickle"), "wb") as hd:
pickle.dump(
{
"fitness1": fitness1,
"chromosome1": chromosome1,
"fitness2": fitness2,
"chromosome2": chromosome2,
"fitness102": fitness102,
"chromosome102": chromosome102,
},
hd
)
print("fitness", fitness1)
# print("chromosome", chromosome1)
print("fitness", fitness2)
# print("chromosome", chromosome2)
print("fitness", fitness102)
def test_evolve_population0(self):
"""
Tests evolve_population() method.
"""
param_values = deepcopy(self.default_param_values)
brkga_params = param_values["params"]
brkga_params.num_independent_populations = 3
brkga_params.num_elite_parents = 1
brkga_params.total_parents = 2
brkga = BrkgaMpIpr(**param_values)
# Not initialized
with self.assertRaises(RuntimeError) as context:
brkga.evolve_population(0)
self.assertEqual(
str(context.exception).strip(),
"The algorithm hasn't been initialized. "
"Call 'initialize()' before 'evolve_population()'")
brkga.initialize()
# Test invalid population indices.
population_index = -1
with self.assertRaises(ValueError) as context:
brkga.evolve_population(population_index)
self.assertEqual(
str(context.exception).strip(), "Population must be in [0, 2]: -1")
population_index = brkga.params.num_independent_populations
with self.assertRaises(ValueError) as context:
brkga.evolve_population(population_index)
self.assertEqual(
str(context.exception).strip(), "Population must be in [0, 2]: 3")
# Save previous and current populations locally
current = deepcopy(brkga._current_populations)
previous = deepcopy(brkga._previous_populations)
########################
# Test if algorithm swaps the populations correctly
########################
for i in range(brkga_params.num_independent_populations):
brkga.evolve_population(i)
# Internal current and previous generation must be different.
self.assertNotEqual(brkga._current_populations[i].chromosomes,
brkga._previous_populations[i].chromosomes)
# The current the from this generation is equal to the previous
# of the next generation.
self.assertEqual(current[i].chromosomes,
brkga._previous_populations[i].chromosomes)
self.assertEqual(current[i].fitness,
brkga._previous_populations[i].fitness)
# The previous of this generation is lost. Just make sure that
# the internal swap gets the new generation, not the current one.
self.assertNotEqual(previous[i].chromosomes,
brkga._current_populations[i].chromosomes)
self.assertNotEqual(previous[i].fitness,
brkga._current_populations[i].fitness)
# end for
print(f"Elapsed time: {time() - self.start_time :.2f}")
