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from pyevolve import G1DList, GSimpleGA, Selectors
from pyevolve import Initializators, Mutators, Consts
import math
# This is the Schaffer F6 Function
# This function has been conceived by Schaffer, it's a
# multimodal function and it's hard for GAs due to the
# large number of local minima, the global minimum is
# at x=0,y=0 and there are many local minima around it
def schafferF6(genome):
t1 = math.sin(math.sqrt(genome[0]**2 + genome[1]**2));
t2 = 1.0 + 0.001*(genome[0]**2 + genome[1]**2);
score = 0.5 + (t1*t1 - 0.5)/(t2*t2)
return score
def run_main():
# Genome instance
genome = G1DList.G1DList(2)
genome.setParams(rangemin=-100.0, rangemax=100.0, bestrawscore=0.0000, rounddecimal=4)
genome.initializator.set(Initializators.G1DListInitializatorReal)
genome.mutator.set(Mutators.G1DListMutatorRealGaussian)
# The evaluator function (objective function)
genome.evaluator.set(schafferF6)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome)
ga.selector.set(Selectors.GRouletteWheel)
ga.setMinimax(Consts.minimaxType["minimize"])
ga.setGenerations(8000)
ga.setMutationRate(0.05)
ga.setPopulationSize(100)
ga.terminationCriteria.set(GSimpleGA.RawScoreCriteria)
# Do the evolution, with stats dump
# frequency of 10 generations
ga.evolve(freq_stats=250)
# Best individual
best = ga.bestIndividual()
print best
print "Best individual score: %.2f" % best.getRawScore()
if __name__ == "__main__":
run_main()
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