toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
def evalOneMax(individual):
return sum(individual),
toolbox.register("evaluate", evalOneMax)
toolbox.register("mate", tools.cxTwoPoints)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)
toolbox.register("map", dtm.map)
def main():
random.seed(64)
pop = toolbox.population(n=300)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", tools.mean)
stats.register("std", tools.std)
stats.register("min", min)
stats.register("max", max)
algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=40, stats=stats,
halloffame=hof, verbose=True)
logging.info("Best individual is %s, %s", hof[0], hof[0].fitness.values)
if __name__ == "__main__":
dtm.setOptions(taskGranularity=0.01)
dtm.start(main)
# the License, or (at your option) any later version.
#
# DEAP is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with DEAP. If not, see <http://www.gnu.org/licenses/>.
"""
Calculation of Pi using a Monte Carlo method.
"""
from math import hypot
from random import random
from deap import dtm
def test(tries):
return sum(hypot(random(), random()) < 1 for i in range(tries))
def calcPi(n, t):
expr = dtm.repeat(test, n, t)
pi_value = 4. * sum(expr) / float(n*t)
print("pi = " + str(pi_value))
return pi_value
dtm.setOptions(setTraceMode=True, taskGranularity=0.01)
dataPi = dtm.start(calcPi, 3000, 5000)
