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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 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)
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("select", tools.selRandom, k=3)
toolbox.register("evaluate", benchmarks.sphere)
def main():
# Differential evolution parameters
CR = 0.25
F = 1
MU = 300
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):
for k, agent in enumerate(pop):
a,b,c = toolbox.select(pop)
y = toolbox.clone(agent)
index = random.randrange(NDIM)
for i, value in enumerate(agent):
if i == index or random.random() < CR:
y[i] = a[i] + F*(b[i]-c[i])
y.fitness.values = toolbox.evaluate(y)
if y.fitness > agent.fitness:
pop[k] = y
hof.update(pop)
record = stats.compile(pop)
logbook.record(gen=g, evals=len(pop), **record)
print(logbook.stream)
print("Best individual is ", hof[0], hof[0].fitness.values[0])
if __name__ == "__main__":
main()
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