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# This file is part of EAP.
#
# EAP 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.
#
# EAP 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 EAP. If not, see <http://www.gnu.org/licenses/>.
import random
import array
import numpy
from itertools import chain
from deap import base
from deap import benchmarks
from deap import creator
from deap import tools
# Problem dimension
NDIM = 10
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin)
def mutDE(y, a, b, c, f):
size = len(y)
for i in range(len(y)):
y[i] = a[i] + f*(b[i]-c[i])
return y
def cxBinomial(x, y, cr):
size = len(x)
index = random.randrange(size)
for i in range(size):
if i == index or random.random() < cr:
x[i] = y[i]
return x
def cxExponential(x, y, cr):
size = len(x)
index = random.randrange(size)
# Loop on the indices index -> end, then on 0 -> index
for i in chain(range(index, size), range(0, index)):
x[i] = y[i]
if random.random() < cr:
break
return x
toolbox = base.Toolbox()
toolbox.register("attr_float", random.uniform, -3, 3)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, NDIM)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("mutate", mutDE, f=0.8)
toolbox.register("mate", cxExponential, cr=0.8)
toolbox.register("select", tools.selRandom, k=3)
toolbox.register("evaluate", benchmarks.griewank)
def main():
# Differential evolution parameters
MU = NDIM * 10
NGEN = 200
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)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
# Evaluate the individuals
fitnesses = toolbox.map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
record = stats.compile(pop)
logbook.record(gen=0, evals=len(pop), **record)
print(logbook.stream)
for g in range(1, NGEN):
children = []
for agent in pop:
# We must clone everything to ensure independence
a, b, c = [toolbox.clone(ind) for ind in toolbox.select(pop)]
x = toolbox.clone(agent)
y = toolbox.clone(agent)
y = toolbox.mutate(y, a, b, c)
z = toolbox.mate(x, y)
del z.fitness.values
children.append(z)
fitnesses = toolbox.map(toolbox.evaluate, children)
for (i, ind), fit in zip(enumerate(children), fitnesses):
ind.fitness.values = fit
if ind.fitness > pop[i].fitness:
pop[i] = ind
hof.update(pop)
record = stats.compile(pop)
logbook.record(gen=g, evals=len(pop), **record)
print(logbook.stream)
print("Best individual is ", hof[0])
print("with fitness", hof[0].fitness.values[0])
return logbook
if __name__ == "__main__":
main()
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