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from pyevolve import G1DList
from pyevolve import Mutators, Crossovers
from pyevolve import Consts, GSimpleGA
from pyevolve import DBAdapters
from random import shuffle
# The "n" in n-queens
BOARD_SIZE = 64
# The n-queens fitness function
def queens_eval(genome):
collisions = 0
for i in xrange(0, BOARD_SIZE):
if i not in genome: return 0
for i in xrange(0, BOARD_SIZE):
col = False
for j in xrange(0, BOARD_SIZE):
if (i != j) and (abs(i-j) == abs(genome[j]-genome[i])):
col = True
if col == True: collisions +=1
return BOARD_SIZE-collisions
def queens_init(genome, **args):
genome.genomeList = range(0, BOARD_SIZE)
shuffle(genome.genomeList)
def run_main():
genome = G1DList.G1DList(BOARD_SIZE)
genome.setParams(bestrawscore=BOARD_SIZE, rounddecimal=2)
genome.initializator.set(queens_init)
genome.mutator.set(Mutators.G1DListMutatorSwap)
genome.crossover.set(Crossovers.G1DListCrossoverCutCrossfill)
genome.evaluator.set(queens_eval)
ga = GSimpleGA.GSimpleGA(genome)
ga.terminationCriteria.set(GSimpleGA.RawScoreCriteria)
ga.setMinimax(Consts.minimaxType["maximize"])
ga.setPopulationSize(100)
ga.setGenerations(250)
ga.setMutationRate(0.02)
ga.setCrossoverRate(1.0)
#sqlite_adapter = DBAdapters.DBSQLite(identify="queens")
#ga.setDBAdapter(sqlite_adapter)
vpython_adapter = DBAdapters.DBVPythonGraph(identify="queens", frequency=1)
ga.setDBAdapter(vpython_adapter)
ga.evolve(freq_stats=10)
best = ga.bestIndividual()
print best
print "Best individual score: %.2f\n" % (best.getRawScore(),)
if __name__ == "__main__":
run_main()
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