def test_permutation_mutation(self):
"""
Chromosome: float array containing x and y values
Selection: roulette-wheel
Crossover operator: uniform crossover
Mutation operator: permutation
Elitism is disabled
Termination criteria: number of generations = 100
Parameters:
population_size: 27
reproduction rate: 0.125
crossover rate: 0.75
mutation rate: 0.125
"""
sys.stdout.write("Starting test_permutation_mutation: ONE-POINT CROSSOVER, PERMUTATION MUTATION, ELITISM DISABLED\n")
reproduction = 0.125
crossover = 0.75
mutation = 0.125
population_size = 27
individual_factory = optimization.XAbsoluteSquareFloatIndividualFactory(crossover_method='one_point', mutation_method='permutation')
termination_criteria = ga.NumberOfGenerationsTerminationCriteria(number_of_generations=100)
solver = ga.GeneticAlgorithm(individual_factory, population_size=population_size, reproduction=reproduction, crossover=crossover, mutation=mutation, elitism=False, termination_criteria=termination_criteria)
repeat = 1
info = pandas.DataFrame([], columns=["generation", "mean", "max", "std", "execution"])
for idx in xrange(repeat):
solver.set_params()
solver.init_population()
solver.evolve()
i = solver.get_generation_info()
i["execution"] = idx
info = info.append(i)
matplotlib.pyplot.plot(i['generation'], i['max'], "coral", label="melhor - permutation", linewidth=2)
matplotlib.pyplot.plot(i['generation'], i['mean'], "cadetblue", label="media - permutation", linewidth=2)
matplotlib.pyplot.plot(i['generation'], i['std'], ".", label="desvio - permutation")
info.to_csv('/home/fabio/sin5006/tests/results/xabsolutesquare/test_permutation_mutation.csv', sep=',', index=False)
base = pandas.read_csv('/home/fabio/sin5006/tests/results/xabsolutesquare/test_base.csv')
matplotlib.pyplot.plot(base["generation"], base["max"], "r", label="melhor - basic mutation", linewidth=2)
matplotlib.pyplot.plot(base["generation"], base["mean"], "b", label="media - basic mutation", linewidth=2)
matplotlib.pyplot.plot(base["generation"], base["std"], "k.", label="desvio - basic mutation")
legend = matplotlib.pyplot.legend(loc='lower right', numpoints=1)
matplotlib.pyplot.xlabel("geracoes")
matplotlib.pyplot.ylabel("fitness")
matplotlib.pyplot.show()
sys.stdout.write("Finished. Results are at: /home/fabio/sin5006/tests/results/xabsolutesquare/test_permutation_mutation.csv\n")
assert True
def test_4(self):
"""
Chromosome: sequence of clients separed by an 'X' when a new vehicle is assigned
Selection: roulette-wheel
Crossover operator: simple random crossover
Mutation operator: simple random mutation
Elitism is enabled
Termination criteria: number of generations = 100
Parameters:
population_size: 97
reproduction rate: 0.25
crossover rate: 0.625
mutation rate: 0.125
"""
fname = './input/A-n80-k10.vrp'
nodes, capacity, distances, demand = self.load_test(fname)
individual_factory = cvrp.CVRPIndividualFactory(
nodes, capacity, distances, demand, individual_type='corrected')
termination_criteria = ga.NumberOfGenerationsTerminationCriteria(
number_of_generations=100)
solver = ga.GeneticAlgorithm(individual_factory,
population_size=97,
reproduction=0.25,
crossover=0.625,
mutation=0.125,
elitism=True,
termination_criteria=termination_criteria)
if self.grid_search:
params = {
"population_size":
numpy.logspace(3, 12, base=2, num=6, dtype=int),
"operators_rate":
filter(
lambda x: sum(x) == 1.0,
itertools.product(numpy.arange(.125, 0.875, .125),
repeat=3)),
"elitism": [True],
"termination_criteria": [
ga.NumberOfGenerationsTerminationCriteria(
number_of_generations=100)
]
}
grid = grid_search.GridSearch(solver, params)
grid.search(0.0)
grid_scores = grid.get_grid_scores()
fname = './results/correction_operator/A-n80-k10.vrp.grid.csv'
grid_scores.to_csv(fname, sep=',', index=False)
sys.stdout.write(
"Finished. Results are at: ./results/correction_operator/A-n80-k10.vrp.grid.csv\n"
)
else:
sys.stdout.write(
"Starting test_4: CORRECTION + SIMPLE RANDOM OPERATORS, ELITISM ENABLED\n"
)
sys.stdout.write("Input: ./tests/vrp/A-n80-k10.vrp\n")
solver.init_population()
solver.evolve()
info = solver.get_generation_info()
fname = './results/correction_operator/A-n80-k10.vrp.csv'
info.to_csv(fname, sep=',', index=False)
plt.plot(info['generation'],
info['min'],
"r",
label="melhor",
linewidth=2)
plt.plot(info['generation'],
info['mean'],
"b",
label="media",
linewidth=2)
plt.plot(info['generation'], info['std'], "k.", label="desvio")
legend = plt.legend(loc='lower right', numpoints=1)
plt.xlabel("geracoes")
plt.ylabel("fitness")
plt.yscale('log')
plt.show()
sys.stdout.write(
"Finished. Results are at: ./results/correction_operator/A-n80-k10.vrp.csv\n"
)
assert True
def test_base(self):
"""
Chromosome: float array containing x and y values
Selection: roulette-wheel
Crossover operator: one-point crossover
Mutation operator: basic (replaces x or y by another valid value)
Elitism is disabled
Termination criteria: number of generations = 100
Parameters:
population_size: 27
reproduction rate: 0.125
crossover rate: 0.75
mutation rate: 0.125
"""
sys.stdout.write(
"Starting test_elitism: ONE-POINT CROSSOVER, BASIC MUTATION, ELITISM ENABLED\n"
)
reproduction = 0.125
crossover = 0.75
mutation = 0.125
population_size = 27
repeat = 1
info = pandas.DataFrame(
[], columns=["generation", "mean", "max", "std", "execution"])
for idx in xrange(repeat):
individual_factory = optimization.RastriginFloatIndividualFactory(
crossover_method='one_point', mutation_method='basic_mutation')
termination_criteria = ga.NumberOfGenerationsTerminationCriteria(
number_of_generations=100)
solver = ga.GeneticAlgorithm(
individual_factory,
population_size=population_size,
reproduction=reproduction,
crossover=crossover,
mutation=mutation,
elitism=False,
termination_criteria=termination_criteria)
solver.set_params()
solver.init_population()
solver.evolve()
i = solver.get_generation_info()
i["execution"] = idx
info = info.append(i)
matplotlib.pyplot.plot(i['generation'],
i['max'],
"r",
label="melhor",
linewidth=2)
matplotlib.pyplot.plot(i['generation'],
i['mean'],
"b",
label="media",
linewidth=2)
matplotlib.pyplot.plot(i['generation'],
i['std'],
"k.",
label="desvio")
info.to_csv(
'/home/fabio/sin5006/tests/results/rastrigin/test_base.csv',
sep=',',
index=False)
legend = matplotlib.pyplot.legend(loc='lower right')
matplotlib.pyplot.xlabel("geracoes")
matplotlib.pyplot.ylabel("fitness")
matplotlib.pyplot.show()
sys.stdout.write(
"Finished. Results are at: /home/fabio/sin5006/tests/results/rastrigin/test_base.csv\n"
)
assert True
