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# This file is part of DEAP.
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# DEAP is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Lesser General Public License as
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# published by the Free Software Foundation, either version 3 of
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# the License, or (at your option) any later version.
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# DEAP is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Lesser General Public License for more details.
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# You should have received a copy of the GNU Lesser General Public
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# License along with DEAP. If not, see <http://www.gnu.org/licenses/>.
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from deap import creator
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from deap import benchmarks
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tools = base.Toolbox()
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creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
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creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin)
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def update(ind, mu, std):
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for i, mu_i in enumerate(mu):
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ind[i] = random.gauss(mu_i,std)
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tools.register("update", update)
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tools.register("evaluate", benchmarks.sphere)
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"""Implements the One-Fifth rule algorithm as expressed in :
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Kern, S., S.D. Muller, N. Hansen, D. Buche, J. Ocenasek and P. Koumoutsakos (2004).
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Learning Probability Distributions in Continuous Evolutionary Algorithms -
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A Comparative Review. Natural Computing, 3(1), pp. 77-112.
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However instead of parent and offspring the algorithm is expressed in terms of
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best and worst. Best is equivalent to the parent, and worst to the offspring.
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Instead of producing a new individual each time, we have defined a function which
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updates the worst individual using the best one as the mean of the gaussian and
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the sigma computed as the standard deviation.
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mu = (random.uniform(interval[0], interval[1]) for _ in xrange(IND_SIZE))
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sigma = (interval[1] - interval[0])/2.0
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alpha = 2.0**(1.0/IND_SIZE)
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best = creator.Individual(mu)
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best.fitness.values = tools.evaluate(best)
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worst = creator.Individual((0.0,)*IND_SIZE)
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for g in xrange(NGEN):
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tools.update(worst, best, sigma)
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worst.fitness.values = tools.evaluate(worst)
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if best.fitness <= worst.fitness:
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best, worst = worst, best
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sigma = sigma * alpha**(-0.25)
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print "Generation", g, "- Fitness:", best.fitness.values[0]
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print "Best individual is ", best, best.fitness.values[0]
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if __name__ == "__main__":
added
removed
examples/es_onefifth.py
