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from pyevolve import G1DList
from pyevolve import GSimpleGA
from pyevolve import Selectors
from pyevolve import DBAdapters
from pyevolve import Statistics
# 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
return score
# Genome instance
genome = G1DList.G1DList(100)
genome.setParams(rangemin=0, rangemax=10)
# The evaluator function (objective function)
genome.evaluator.set(eval_func)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome, 666)
ga.setGenerations(80)
ga.setMutationRate(0.2)
# Create DB Adapter and set as adapter
#sqlite_adapter = DBAdapters.DBSQLite(identify="ex6", resetDB=True)
#ga.setDBAdapter(sqlite_adapter)
# Using CSV Adapter
#csvfile_adapter = DBAdapters.DBFileCSV()
#ga.setDBAdapter(csvfile_adapter)
# Using the URL Post Adapter
# urlpost_adapter = DBAdapters.DBURLPost(url="http://whatismyip.oceanus.ro/server_variables.php", post=False)
# ga.setDBAdapter(urlpost_adapter)
# Do the evolution, with stats dump
# frequency of 10 generations
ga.evolve(freq_stats=10)
# Best individual
#print ga.bestIndividual()
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