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@aidancbrady
Created February 20, 2019 18:33
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import java.util.Arrays;
import java.util.Random;
import dist.DiscreteDependencyTree;
import dist.DiscretePermutationDistribution;
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.SwapNeighbor;
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.ga.SwapMutation;
import opt.ga.UniformCrossOver;
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 IterationTest {
/** Random number generator */
private static final Random random = new Random();
/** The number of items */
private static final int NUM_ITEMS = 40;
/** The number of copies each */
private static final int COPIES_EACH = 4;
/** The maximum value for a single element */
private static final double MAX_VALUE = 50;
/** The maximum weight for a single element */
private static final double MAX_WEIGHT = 50;
/** The maximum weight for the knapsack */
private static final double MAX_KNAPSACK_WEIGHT =
MAX_WEIGHT * NUM_ITEMS * COPIES_EACH * .4;
public static void main(String[] args) {
int[] copies = new int[NUM_ITEMS];
Arrays.fill(copies, COPIES_EACH);
double[] values = new double[NUM_ITEMS];
double[] weights = new double[NUM_ITEMS];
for (int i = 0; i < NUM_ITEMS; i++) {
values[i] = random.nextDouble() * MAX_VALUE;
weights[i] = random.nextDouble() * MAX_WEIGHT;
}
int[] ranges = new int[NUM_ITEMS];
Arrays.fill(ranges, COPIES_EACH + 1);
EvaluationFunction ef = new KnapsackEvaluationFunction(values, weights, MAX_KNAPSACK_WEIGHT, copies);
Distribution odd = new DiscreteUniformDistribution(ranges);
NeighborFunction nf = new DiscreteChangeOneNeighbor(ranges);
MutationFunction mf = new DiscreteChangeOneMutation(ranges);
CrossoverFunction cf = new UniformCrossOver();
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);
for(int i = 0; i < 5000; i ++) {
rhc.train();
System.out.println(ef.value(rhc.getOptimal()));
}*/
/* SimulatedAnnealing sa = new SimulatedAnnealing(100, .95, hcp);
for(int i = 0; i < 5000; i ++) {
sa.train();
System.out.println(ef.value(sa.getOptimal()));
}*/
StandardGeneticAlgorithm ga = new StandardGeneticAlgorithm(200, 150, 25, gap);
for(int i = 0; i < 5000; i ++) {
ga.train();
System.out.println(ef.value(ga.getOptimal()));
}
/* MIMIC mimic = new MIMIC(200, 100, pop);
for(int i = 0; i < 5000; i ++) {
mimic.train();
System.out.println(ef.value(mimic.getOptimal()));
}*/
}
private static long timestamp;
public static void startRecording(String s) {
timestamp = System.currentTimeMillis();
System.out.println("Recording time for: " + s);
}
public static void stopRecording() {
long diff = System.currentTimeMillis()-timestamp;
System.out.println("Time elapsed: " + diff);
}
}
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