public static void main(String[] args) { if (args.length < 2) { System.out.println("Provide a input size and repeat count"); System.exit(0); } int N = Integer.parseInt(args[0]); if (N < 0) { System.out.println(" N cannot be negaitve."); System.exit(0); } Random random = new Random(); // create the random points double[][] points = new double[N][2]; for (int i = 0; i < points.length; i++) { points[i][0] = random.nextDouble(); points[i][1] = random.nextDouble(); } int iterations = Integer.parseInt(args[1]); // for rhc, sa, and ga we use a permutation based encoding TravelingSalesmanEvaluationFunction ef = new TravelingSalesmanRouteEvaluationFunction(points); Distribution odd = new DiscretePermutationDistribution(N); NeighborFunction nf = new SwapNeighbor(); MutationFunction mf = new SwapMutation(); CrossoverFunction cf = new TravelingSalesmanCrossOver(ef); HillClimbingProblem hcp = new GenericHillClimbingProblem(ef, odd, nf); GeneticAlgorithmProblem gap = new GenericGeneticAlgorithmProblem(ef, odd, mf, cf); System.out.println("Randomized Hill Climbing\n---------------------------------"); for (int i = 0; i < iterations; i++) { RandomizedHillClimbing rhc = new RandomizedHillClimbing(hcp); long t = System.nanoTime(); FixedIterationTrainer fit = new FixedIterationTrainer(rhc, 200000); fit.train(); System.out.println( ef.value(rhc.getOptimal()) + ", " + (((double) (System.nanoTime() - t)) / 1e9d)); } System.out.println("Simulated Annealing \n---------------------------------"); for (int i = 0; i < iterations; i++) { SimulatedAnnealing sa = new SimulatedAnnealing(1E12, .95, hcp); long t = System.nanoTime(); FixedIterationTrainer fit = new FixedIterationTrainer(sa, 200000); fit.train(); System.out.println( ef.value(sa.getOptimal()) + ", " + (((double) (System.nanoTime() - t)) / 1e9d)); } System.out.println("Genetic Algorithm\n---------------------------------"); for (int i = 0; i < iterations; i++) { StandardGeneticAlgorithm ga = new StandardGeneticAlgorithm(200, 150, 10, gap); long t = System.nanoTime(); FixedIterationTrainer fit = new FixedIterationTrainer(ga, 1000); fit.train(); System.out.println( ef.value(ga.getOptimal()) + ", " + (((double) (System.nanoTime() - t)) / 1e9d)); } System.out.println("MIMIC \n---------------------------------"); // for mimic we use a sort encoding int[] ranges = new int[N]; Arrays.fill(ranges, N); odd = new DiscreteUniformDistribution(ranges); Distribution df = new DiscreteDependencyTree(.1, ranges); for (int i = 0; i < iterations; i++) { ProbabilisticOptimizationProblem pop = new GenericProbabilisticOptimizationProblem(ef, odd, df); MIMIC mimic = new MIMIC(200, 60, pop); long t = System.nanoTime(); FixedIterationTrainer fit = new FixedIterationTrainer(mimic, 1000); fit.train(); System.out.println( ef.value(mimic.getOptimal()) + ", " + (((double) (System.nanoTime() - t)) / 1e9d)); } }