Merge pull request pushkar#8 from has207/master

Add FourPeaksTest based on ContinuousPeaksTest

src/opt/test/FourPeaksTest.java

@@ -0,0 +1,72 @@
+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;
+
+/**
+ * Copied from ContinuousPeaksTest
+ * @version 1.0
+ */
+public class FourPeaksTest {
+    /** The n value */
+    private static final int N = 200;
+    /** The t value */
+    private static final int T = N / 5;
+
+    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("RHC: " + ef.value(rhc.getOptimal()));
+
+        SimulatedAnnealing sa = new SimulatedAnnealing(1E11, .95, hcp);
+        fit = new FixedIterationTrainer(sa, 200000);
+        fit.train();
+        System.out.println("SA: " + ef.value(sa.getOptimal()));
+
+        StandardGeneticAlgorithm ga = new StandardGeneticAlgorithm(200, 100, 10, gap);
+        fit = new FixedIterationTrainer(ga, 1000);
+        fit.train();
+        System.out.println("GA: " + ef.value(ga.getOptimal()));
+
+        MIMIC mimic = new MIMIC(200, 20, pop);
+        fit = new FixedIterationTrainer(mimic, 1000);
+        fit.train();
+        System.out.println("MIMIC: " + ef.value(mimic.getOptimal()));
+    }
+}