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
|
from pyevolve import G2DList
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) too
if chromosome[i][j]==0:
score += 0.1
return score
def run_main():
# Genome instance
genome = G2DList.G2DList(8, 5)
genome.setParams(rangemin=0, rangemax=100)
# The evaluator function (objective function)
genome.evaluator.set(eval_func)
genome.crossover.set(Crossovers.G2DListCrossoverSingleHPoint)
genome.mutator.set(Mutators.G2DListMutatorIntegerRange)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome)
ga.setGenerations(800)
# Do the evolution, with stats dump
# frequency of 10 generations
ga.evolve(freq_stats=100)
# Best individual
print ga.bestIndividual()
if __name__ == "__main__":
run_main()
|
