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from pyevolve import G1DList
from pyevolve import GSimpleGA
from pyevolve import Selectors
from pyevolve import Statistics
from pyevolve import DBAdapters
# This function is the evaluation function, we want
# to give high score to more zero'ed chromosomes
def eval_func(genome):
score = 0.0
# iterate over the chromosome
# The same as "score = len(filter(lambda x: x==0, genome))"
for value in genome:
if value==0:
score += 1
return score
def run_main():
# Genome instance, 1D List of 50 elements
genome = G1DList.G1DList(50)
# Sets the range max and min of the 1D List
genome.setParams(rangemin=0, rangemax=10)
# The evaluator function (evaluation function)
genome.evaluator.set(eval_func)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome)
# Set the Roulette Wheel selector method, the number of generations and
# the termination criteria
ga.selector.set(Selectors.GRouletteWheel)
ga.setGenerations(500)
ga.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
# Sets the DB Adapter, the resetDB flag will make the Adapter recreate
# the database and erase all data every run, you should use this flag
# just in the first time, after the pyevolve.db was created, you can
# omit it.
sqlite_adapter = DBAdapters.DBSQLite(identify="ex1", resetDB=True)
ga.setDBAdapter(sqlite_adapter)
# Do the evolution, with stats dump
# frequency of 20 generations
ga.evolve(freq_stats=20)
# Best individual
print ga.bestIndividual()
if __name__ == "__main__":
run_main()
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