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from pyevolve import GSimpleGA
from pyevolve import GTree
from pyevolve import Crossovers
from pyevolve import Mutators
import time
import random
def eval_func(chromosome):
score = 0.0
# If you want to add score values based
# in the height of the Tree, the extra
# code is commented.
#height = chromosome.getHeight()
for node in chromosome:
score += (100 - node.getData())*0.1
#if height <= chromosome.getParam("max_depth"):
# score += (score*0.8)
return score
def run_main():
genome = GTree.GTree()
root = GTree.GTreeNode(2)
genome.setRoot(root)
genome.processNodes()
genome.setParams(max_depth=3, max_siblings=2, method="grow")
genome.evaluator.set(eval_func)
genome.crossover.set(Crossovers.GTreeCrossoverSinglePointStrict)
ga = GSimpleGA.GSimpleGA(genome)
ga.setGenerations(100)
ga.setMutationRate(0.05)
ga.evolve(freq_stats=10)
print ga.bestIndividual()
if __name__ == "__main__":
run_main()
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