/usr/share/doc/python-pyevolve-doc/examples/pyevolve_ex11_allele.py is in python-pyevolve-doc 0.6~rc1+svn398+dfsg-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | 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 xrange(11):
a = GAllele.GAlleleRange(0, i)
setOfAlleles.add(a)
# From 11 to 19 we can have a set
# of elements
for i in xrange(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()
|