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from pyevolve import G2DBinaryString
from pyevolve import GSimpleGA
from pyevolve import Selectors
from pyevolve import Crossovers
from pyevolve import Mutators
# This function is the evaluation function, we want
# to give high score to more zero'ed chromosomes
def eval_func(chromosome):
score = 0.0
# iterate over the chromosome
for i in xrange(chromosome.getHeight()):
for j in xrange(chromosome.getWidth()):
# You can use the chromosome.getItem(i, j)
if chromosome[i][j]==0:
score += 0.1
return score
# Genome instance
genome = G2DBinaryString.G2DBinaryString(8, 5)
# The evaluator function (objective function)
genome.evaluator.set(eval_func)
genome.crossover.set(Crossovers.G2DBinaryStringXSingleHPoint)
genome.mutator.set(Mutators.G2DBinaryStringMutatorSwap)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome)
ga.setGenerations(200)
# Do the evolution, with stats dump
# frequency of 10 generations
ga.evolve(freq_stats=10)
# Best individual
print ga.bestIndividual()
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