A simple GA with - roulette-wheel sampling - population size 100- single point crossover rate 0.7- bitwise mutation rate 0.002- chromosome length 100- generations 500

main.py

__author__ = 'yafeunteun'

import pyevolve
from pyevolve import  G1DList, GSimpleGA, Selectors

# This function is the evaluation function, we want
# to give high score to more one'ed chromosomes
def eval_func(chromosome):
   score = 0.0

   # iterate over the chromosome elements (items)
   for value in chromosome:
      if value==1:
         score += 1.0

   return score

def main():
    # Genome instance
    genome = G1DList.G1DList(100)

    # The evaluator function (objective function)
    genome.evaluator.set(eval_func)

    genome.setParams(rangemin=0, rangemax=1)

    ga = GSimpleGA.GSimpleGA(genome)

    ga.selector.set(Selectors.GRouletteWheel)
    ga.setCrossoverRate(0.7)
    ga.setMutationRate(0.002)
    ga.setGenerations(500)

    # Do the evolution, with stats dump
    # frequency of 10 generations
    ga.evolve(freq_stats=10)

    # Best individual
    print ga.bestIndividual()


if __name__ == "__main__":
    main()