pyevolve_ex11_allele.py

from pyevolve import G1DList
from pyevolve import GSimpleGA
from pyevolve import Mutators
from pyevolve import Initializators
from pyevolve import GAllele


# 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 += 0.5

    # Remember from the allele set defined above
    # this value 'a' is possible at this position
    if chromosome[18] == 'a':
        score += 1.0

    # Remember from the allele set defined above
    # this value 'xxx' is possible at this position
    if chromosome[12] == 'xxx':
        score += 1.0

    return score


def run_main():
    # Genome instance
    setOfAlleles = GAllele.GAlleles()

    # From 0 to 10 we can have only some
    # defined ranges of integers
    for i in range(11):
        a = GAllele.GAlleleRange(0, i)
        setOfAlleles.add(a)

    # From 11 to 19 we can have a set
    # of elements
    for i in range(11, 20):
        # You can even add objects instead of strings or
        # primitive values
        a = GAllele.GAlleleList(['a', 'b', 'xxx', 666, 0])
        setOfAlleles.add(a)

    genome = G1DList.G1DList(20)
    genome.setParams(allele=setOfAlleles)

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

    # This mutator and initializator will take care of
    # initializing valid individuals based on the allele set
    # that we have defined before
    genome.mutator.set(Mutators.G1DListMutatorAllele)
    genome.initializator.set(Initializators.G1DListInitializatorAllele)

    # Genetic Algorithm Instance
    ga = GSimpleGA.GSimpleGA(genome)
    ga.setGenerations(40)

    # Do the evolution, with stats dump
    # frequency of 10 generations
    ga.evolve(freq_stats=5)

    # Best individual
    print(ga.bestIndividual())


if __name__ == "__main__":
    run_main()