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
|
from pyevolve import GSimpleGA
from pyevolve import G1DList
from pyevolve import Selectors
from pyevolve import Initializators, Mutators
# Find negative element
def eval_func(genome):
score = 0.0
for element in genome:
if element < 0: score += 0.1
return score
def run_main():
# Genome instance
genome = G1DList.G1DList(20)
genome.setParams(rangemin=-6.0, rangemax=6.0)
# Change the initializator to Real values
genome.initializator.set(Initializators.G1DListInitializatorReal)
# Change the mutator to Gaussian Mutator
genome.mutator.set(Mutators.G1DListMutatorRealGaussian)
# The evaluator function (objective function)
genome.evaluator.set(eval_func)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome)
ga.selector.set(Selectors.GRouletteWheel)
ga.setGenerations(100)
# Do the evolution
ga.evolve(freq_stats=10)
# Best individual
print ga.bestIndividual()
if __name__ == "__main__":
run_main()
|
