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from pyevolve import G1DBinaryString
from pyevolve import GSimpleGA
from pyevolve import Selectors
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 value in chromosome:
if value == 0:
score += 0.1
return score
def run_main():
# Genome instance
genome = G1DBinaryString.G1DBinaryString(50)
# The evaluator function (objective function)
genome.evaluator.set(eval_func)
genome.mutator.set(Mutators.G1DBinaryStringMutatorFlip)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome)
ga.selector.set(Selectors.GTournamentSelector)
ga.setGenerations(70)
# Do the evolution, with stats dump
# frequency of 10 generations
ga.evolve(freq_stats=20)
# Best individual
print ga.bestIndividual()
if __name__ == "__main__":
run_main()
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