def setUp(self):
"""
Sets up some configurations.
"""
Test.maxDiff = None
# For travis-ci output.
self.start_time = time()
self.chromosome_size = 100
self.default_brkga_params = BrkgaParams()
self.default_brkga_params.population_size = 10
self.default_brkga_params.elite_percentage = 0.3
self.default_brkga_params.mutants_percentage = 0.1
self.default_brkga_params.num_elite_parents = 1
self.default_brkga_params.total_parents = 2
self.default_brkga_params.bias_type = BiasFunctionType.LOGINVERSE
self.default_brkga_params.num_independent_populations = 3
self.default_brkga_params.pr_number_pairs = 0
self.default_brkga_params.pr_minimum_distance = 0.0
self.default_brkga_params.pr_type = PathRelinkingType.DIRECT
self.default_brkga_params.pr_selection = PathRelinkingSelection.BESTSOLUTION
self.default_brkga_params.alpha_block_size = 1.0
self.default_brkga_params.pr_percentage = 1.0
self.instance = Instance(self.chromosome_size)
self.sum_decoder = SumDecode(self.instance)
self.rank_decoder = RankDecode(self.instance)
self.default_param_values = {
"decoder": self.sum_decoder,
"sense": Sense.MAXIMIZE,
"seed": 98747382473209,
"chromosome_size": self.chromosome_size,
"params": self.default_brkga_params,
"evolutionary_mechanism_on": True,
"chrmosome_type": BaseChromosome
}
def gen_conf5():
param_values = deepcopy(default_param_values)
brkga_params = param_values["params"]
chromosome_size = 100
instance = Instance(chromosome_size)
brkga_params.population_size = 100
brkga_params.elite_percentage = 0.30
brkga_params.mutants_percentage = 0.20
brkga_params.num_elite_parents = 2
brkga_params.total_parents = 3
brkga_params.bias_type = BiasFunctionType.LOGINVERSE
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"] = 4659930950303
param_values["chromosome_size"] = chromosome_size
param_values["evolutionary_mechanism_on"] = True
print("\n> Building configuration 5")
brkga = BrkgaMpIpr(**param_values)
brkga.initialize()
print("> Writing configuration 5")
with open(os.path.join(STATE_DIR, "state5.pickle"), "wb") as hd:
pickle.dump(brkga, hd)
print("> Evolving one generation...")
brkga.evolve()
fitness1 = brkga.get_best_fitness()
chromosome1 = brkga.get_best_chromosome()
print("> Evolving 10 generations...")
brkga.evolve(10)
fitness10 = brkga.get_best_fitness()
chromosome10 = brkga.get_best_chromosome()
print("> Evolving 100 generations...")
brkga.evolve(100)
fitness100 = brkga.get_best_fitness()
chromosome100 = brkga.get_best_chromosome()
with open(os.path.join(SOLUTION_DIR, "best_solution5.pickle"), "wb") as hd:
pickle.dump(
{
"fitness1": fitness1,
"chromosome1": chromosome1,
"fitness10": fitness10,
"chromosome10": chromosome10,
"fitness100": fitness100,
"chromosome100": chromosome100
},
hd
)
print("fitness", fitness1)
print("fitness", fitness10)
print("fitness", fitness100)
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_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 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
)
default_brkga_params = BrkgaParams()
default_brkga_params.population_size = 10
default_brkga_params.elite_percentage = 0.3
default_brkga_params.mutants_percentage = 0.1
default_brkga_params.num_elite_parents = 1
default_brkga_params.total_parents = 2
default_brkga_params.bias_type = BiasFunctionType.LOGINVERSE
default_brkga_params.num_independent_populations = 3
default_brkga_params.pr_number_pairs = 0
default_brkga_params.pr_minimum_distance = 0.0
default_brkga_params.pr_type = PathRelinkingType.DIRECT
default_brkga_params.pr_selection = PathRelinkingSelection.BESTSOLUTION
default_brkga_params.alpha_block_size = 1.0
default_brkga_params.pr_percentage = 1.0
instance = Instance(chromosome_size)
sum_decoder = SumDecode(instance)
rank_decoder = RankDecode(instance)
default_param_values = {
"decoder": sum_decoder,
"sense": Sense.MAXIMIZE,
"seed": 98747382473209,
"chromosome_size": chromosome_size,
"params": default_brkga_params,
"evolutionary_mechanism_on": True,
"chrmosome_type": BaseChromosome
}
###############################################################################
# Configuration 1