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# This file is part of EAP.
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# EAP 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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# EAP 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 EAP. If not, see <http://www.gnu.org/licenses/>.
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from deap import benchmarks
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from deap import creator
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from deap import tools
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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 mutDE(y, a, b, c, f):
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for i in range(len(y)):
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y[i] = a[i] + f*(b[i]-c[i])
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def cxBinomial(x, y, cr):
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index = random.randrange(size)
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if i == index or random.random() < cr:
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def cxExponential(x, y, cr):
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index = random.randrange(size)
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# Loop on the indices index -> end, then on 0 -> index
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for i in range(index, size) + range(0, index):
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if random.random() < cr:
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toolbox = base.Toolbox()
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toolbox.register("attr_float", random.uniform, -3, 3)
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toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, NDIM)
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toolbox.register("population", tools.initRepeat, list, toolbox.individual)
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toolbox.register("mutate", mutDE, f=0.8)
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toolbox.register("mate", cxExponential, cr=0.8)
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toolbox.register("select", tools.selRandom, k=3)
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toolbox.register("evaluate", benchmarks.griewank)
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# Differential evolution parameters
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pop = toolbox.population(n=MU);
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hof = tools.HallOfFame(1)
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stats = tools.Statistics(lambda ind: ind.fitness.values)
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stats.register("Avg", tools.mean)
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stats.register("Std", tools.std)
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stats.register("Min", min)
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stats.register("Max", max)
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# Evaluate the individuals
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fitnesses = toolbox.map(toolbox.evaluate, pop)
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for ind, fit in zip(pop, fitnesses):
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ind.fitness.values = fit
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# We must clone everything to ensure independance
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a, b, c = [toolbox.clone(ind) for ind in toolbox.select(pop)]
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x = toolbox.clone(agent)
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y = toolbox.clone(agent)
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y = toolbox.mutate(y, a, b, c)
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z = toolbox.mate(x, y)
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fitnesses = toolbox.map(toolbox.evaluate, children)
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for ind, fit, i in zip(children, fitnesses, range(len(fitnesses))):
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ind.fitness.values = fit
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if ind.fitness > pop[i].fitness:
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print "-- Generation %i --" % g
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print "Best individual is ", hof[0]
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print "with fitness", hof[0].fitness.values[0]
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if __name__ == "__main__":
added
removed
examples/de_sphere.py
