N = 200 T = N / 5 fill = [2] * N ranges = array('i', fill) ef = FourPeaksEvaluationFunction(T) odd = DiscreteUniformDistribution(ranges) nf = DiscreteChangeOneNeighbor(ranges) mf = DiscreteChangeOneMutation(ranges) cf = SingleCrossOver() df = DiscreteDependencyTree(.1, ranges) hcp = GenericHillClimbingProblem(ef, odd, nf) gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf) pop = GenericProbabilisticOptimizationProblem(ef, odd, df) rhc = RandomizedHillClimbing(hcp) fit = FixedIterationTrainer(rhc, 200000) fit.train() print "RHC: " + str(ef.value(rhc.getOptimal())) sa = SimulatedAnnealing(1E11, .95, hcp) fit = FixedIterationTrainer(sa, 200000) fit.train() print "SA: " + str(ef.value(sa.getOptimal())) ga = StandardGeneticAlgorithm(200, 100, 10, gap) fit = FixedIterationTrainer(ga, 1000) fit.train() print "GA: " + str(ef.value(ga.getOptimal())) mimic = MIMIC(200, 20, pop)
N=200 T=N/5 fill = [2] * N ranges = list(array('i', fill)) ef = FourPeaksEvaluationFunction(T) odd = DiscreteUniformDistribution(ranges) nf = DiscreteChangeOneNeighbor(ranges) mf = DiscreteChangeOneMutation(ranges) cf = SingleCrossOver() df = DiscreteDependencyTree(.1, ranges) hcp = GenericHillClimbingProblem(ef, odd, nf) gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf) pop = GenericProbabilisticOptimizationProblem(ef, odd, df) rhc = RandomizedHillClimbing(hcp) fit = FixedIterationTrainer(rhc, 200000) fit.train() print "RHC: " + str(ef.value(rhc.getOptimal())) sa = SimulatedAnnealing(1E11, .95, hcp) fit = FixedIterationTrainer(sa, 200000) fit.train() print "SA: " + str(ef.value(sa.getOptimal())) ga = StandardGeneticAlgorithm(200, 100, 10, gap) fit = FixedIterationTrainer(ga, 1000) fit.train() print "GA: " + str(ef.value(ga.getOptimal())) mimic = MIMIC(200, 20, pop)
N = 50 points = [[0 for x in xrange(2)] for x in xrange(N)] for i in range(0, len(points)): points[i][0] = random.random() points[i][1] = random.random() ef = TravelingSalesmanRouteEvaluationFunction(points) odd = DiscretePermutationDistribution(N) nf = SwapNeighbor() mf = SwapMutation() cf = TravelingSalesmanCrossOver(ef) hcp = GenericHillClimbingProblem(ef, odd, nf) gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf) rhc = RandomizedHillClimbing(hcp) fit = FixedIterationTrainer(rhc, 200000) fit.train() print "RHC Inverse of Distance: " + str(ef.value(rhc.getOptimal())) print "Route:" path = [] for x in range(0,N): path.append(rhc.getOptimal().getDiscrete(x)) print path sa = SimulatedAnnealing(1E12, .999, hcp) fit = FixedIterationTrainer(sa, 200000) fit.train() print "SA Inverse of Distance: " + str(ef.value(sa.getOptimal())) print "Route:" path = []