package opt.test;
import java.util.Arrays;
import dist.DiscreteDependencyTree;
import dist.DiscreteUniformDistribution;
import dist.Distribution;
import opt.DiscreteChangeOneNeighbor;
import opt.EvaluationFunction;
import opt.GenericHillClimbingProblem;
import opt.HillClimbingProblem;
import opt.NeighborFunction;
import opt.RandomizedHillClimbing;
import opt.SimulatedAnnealing;
import opt.example.*;
import opt.ga.CrossoverFunction;
import opt.ga.DiscreteChangeOneMutation;
import opt.ga.SingleCrossOver;
import opt.ga.GenericGeneticAlgorithmProblem;
import opt.ga.GeneticAlgorithmProblem;
import opt.ga.MutationFunction;
import opt.ga.StandardGeneticAlgorithm;
import opt.prob.GenericProbabilisticOptimizationProblem;
import opt.prob.MIMIC;
import opt.prob.ProbabilisticOptimizationProblem;
import shared.FixedIterationTrainer;
/**
* A test using the flip flop evaluation function
* @author Andrew Guillory gtg008g@mail.gatech.edu
* @version 1.0
*/
public class FlipFlopTest {
/** The n value */
private static final int N = 80;
private static final int T = N/10;
public static void main(String[] args) {
int[] ranges = new int[N];
Arrays.fill(ranges, 2);
EvaluationFunction ef = new FourPeaksEvaluationFunction(T);
Distribution odd = new DiscreteUniformDistribution(ranges);
NeighborFunction nf = new DiscreteChangeOneNeighbor(ranges);
MutationFunction mf = new DiscreteChangeOneMutation(ranges);
CrossoverFunction cf = new SingleCrossOver();
Distribution df = new DiscreteDependencyTree(.1, ranges);
HillClimbingProblem hcp = new GenericHillClimbingProblem(ef, odd, nf);
GeneticAlgorithmProblem gap = new GenericGeneticAlgorithmProblem(ef, odd, mf, cf);
ProbabilisticOptimizationProblem pop = new GenericProbabilisticOptimizationProblem(ef, odd, df);
RandomizedHillClimbing rhc = new RandomizedHillClimbing(hcp);
FixedIterationTrainer fit = new FixedIterationTrainer(rhc, 200000);
fit.train();
System.out.println(ef.value(rhc.getOptimal()));
SimulatedAnnealing sa = new SimulatedAnnealing(100, .95, hcp);
fit = new FixedIterationTrainer(sa, 200000);
fit.train();
System.out.println(ef.value(sa.getOptimal()));
StandardGeneticAlgorithm ga = new StandardGeneticAlgorithm(200, 100, 20, gap);
fit = new FixedIterationTrainer(ga, 1000);
fit.train();
System.out.println(ef.value(ga.getOptimal()));
MIMIC mimic = new MIMIC(200, 5, pop);
fit = new FixedIterationTrainer(mimic, 1000);
fit.train();
System.out.println(ef.value(mimic.getOptimal()));
}
}