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fctmin.py
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fctmin.py
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# This file is part of DEAP.
#
# DEAP is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 3 of
# 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/>.
import array
import random
import numpy
from deap import algorithms
from deap import base
from deap import benchmarks
from deap import creator
from deap import tools
IND_SIZE = 30
MIN_VALUE = 4
MAX_VALUE = 5
MIN_STRATEGY = 0.5
MAX_STRATEGY = 3
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", array.array, typecode="d", fitness=creator.FitnessMin, strategy=None)
creator.create("Strategy", array.array, typecode="d")
# Individual generator
def generateES(icls, scls, size, imin, imax, smin, smax):
ind = icls(random.uniform(imin, imax) for _ in range(size))
ind.strategy = scls(random.uniform(smin, smax) for _ in range(size))
return ind
def checkStrategy(minstrategy):
def decorator(func):
def wrappper(*args, **kargs):
children = func(*args, **kargs)
for child in children:
for i, s in enumerate(child.strategy):
if s < minstrategy:
child.strategy[i] = minstrategy
return children
return wrappper
return decorator
toolbox = base.Toolbox()
toolbox.register("individual", generateES, creator.Individual, creator.Strategy,
IND_SIZE, MIN_VALUE, MAX_VALUE, MIN_STRATEGY, MAX_STRATEGY)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("mate", tools.cxESBlend, alpha=0.1)
toolbox.register("mutate", tools.mutESLogNormal, c=1.0, indpb=0.03)
toolbox.register("select", tools.selTournament, tournsize=3)
toolbox.register("evaluate", benchmarks.sphere)
toolbox.decorate("mate", checkStrategy(MIN_STRATEGY))
toolbox.decorate("mutate", checkStrategy(MIN_STRATEGY))
def main():
random.seed()
MU, LAMBDA = 10, 100
pop = toolbox.population(n=MU)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
pop, logbook = algorithms.eaMuCommaLambda(pop, toolbox, mu=MU, lambda_=LAMBDA,
cxpb=0.6, mutpb=0.3, ngen=500, stats=stats, halloffame=hof)
return pop, logbook, hof
if __name__ == "__main__":
main()