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from pyevolve import G1DList, GSimpleGA, Selectors
from pyevolve import Initializators, Mutators, Consts, DBAdapters
import math
# This is the Rastringin Function, a deception function
def ackley(xlist):
sum1 = 0
score = 0
n = len(xlist)
for i in xrange(n):
sum1 += xlist[i]*xlist[i]
t1 = math.exp(-0.2*(math.sqrt((1.0/5.0)*sum1)))
sum1 = 0
for i in xrange(n):
sum1 += math.cos(2.0*math.pi*xlist[i]);
t2 = math.exp((1.0/5.0)*sum1);
score = 20 + math.exp(1) - 20 * t1 - t2;
return score
def run_main():
# Genome instance
genome = G1DList.G1DList(5)
genome.setParams(rangemin=-8, rangemax=8, bestrawscore=0.00, rounddecimal=2)
genome.initializator.set(Initializators.G1DListInitializatorReal)
genome.mutator.set(Mutators.G1DListMutatorRealGaussian)
# The evaluator function (objective function)
genome.evaluator.set(ackley)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome)
ga.setMinimax(Consts.minimaxType["minimize"])
ga.setGenerations(1000)
ga.setMutationRate(0.04)
ga.terminationCriteria.set(GSimpleGA.RawScoreCriteria)
# Create DB Adapter and set as adapter
# sqlite_adapter = DBAdapters.DBSQLite(identify="ackley")
# ga.setDBAdapter(sqlite_adapter)
# Do the evolution, with stats dump
# frequency of 10 generations
ga.evolve(freq_stats=50)
# Best individual
best = ga.bestIndividual()
print "\nBest individual score: %.2f" % (best.getRawScore(),)
print best
if __name__ == "__main__":
run_main()
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