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- package holeg.algorithm.binary;
- import holeg.api.AlgorithmFrameworkFlex;
- import java.util.ArrayList;
- import java.util.List;
- import java.util.ListIterator;
- import java.util.TreeSet;
- import javax.swing.JFrame;
- public class GaAlgorithm extends AlgorithmFrameworkFlex {
- /**
- * Should be even.
- */
- private int popsize = 20;
- private int maxGenerations = 100;
- private double tournamentSize = 2.0;
- private double swapProbability = 0.02;
- private double mutateProbability = 0.02;
- private boolean useIntervalMutation = false;
- //private double mutateProbabilityInterval = 0.01;
- private double maxMutationPercent = 0.01;
- private boolean moreInformation = false;
- public GaAlgorithm() {
- super();
- addIntParameter("Population size", popsize, intValue -> popsize = intValue, () -> popsize, 1);
- addIntParameter("Generations", maxGenerations, intValue -> maxGenerations = intValue,
- () -> maxGenerations, 1);
- addDoubleParameter("Tournament size", tournamentSize,
- doubleValue -> tournamentSize = doubleValue, () -> tournamentSize, 1.0);
- addDoubleParameter("Swap probability", swapProbability,
- doubleValue -> swapProbability = doubleValue, () -> swapProbability, 0.0, 1.0);
- addDoubleParameter("Mutation probability", mutateProbability,
- doubleValue -> mutateProbability = doubleValue, () -> mutateProbability, 0.0, 1.0);
- addBooleanParameter("Interval-based mutation", useIntervalMutation,
- booleanValue -> useIntervalMutation = booleanValue);
- //addDoubleParameter("mutateProbabilityInterval", mutateProbabilityInterval, doubleValue -> mutateProbabilityInterval = doubleValue, () -> mutateProbabilityInterval, 0.0, 1.0);
- addDoubleParameter("Mutation severity (% of problem size)", maxMutationPercent,
- doubleValue -> maxMutationPercent = doubleValue, () -> maxMutationPercent, 0.0, 1.0);
- addBooleanParameter("Detailed Information", moreInformation,
- booleanValue -> moreInformation = booleanValue);
- }
- public static void main(String[] args) {
- JFrame newFrame = new JFrame("exampleWindow");
- GaAlgorithm instance = new GaAlgorithm();
- newFrame.setContentPane(instance.getPanel());
- newFrame.pack();
- newFrame.setVisible(true);
- newFrame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
- }
- @Override
- protected int getProgressBarMaxCount() {
- return this.maxGenerations * this.popsize * this.rounds + rounds;
- }
- @Override
- protected Individual executeAlgo() {
- Individual best = new Individual();
- best.position = extractPositionAndAccess();
- if (moreInformation) {
- console.println("Bit-Array_length: " + best.position.size());
- }
- best.fitness = evaluatePosition(best.position);
- List<Double> runList = new ArrayList<Double>();
- runList.add(best.fitness);
- console.print("Start with: " + best.fitness);
- if (moreInformation) {
- console.println("");
- }
- int problemSize = best.position.size();
- List<Individual> population = initPopuluationRandom(problemSize, best);
- if (moreInformation) {
- console.println("Size To Test:" + population.size());
- }
- for (int generation = 0; generation < maxGenerations; generation++) {
- if (moreInformation) {
- console.println("Generation" + generation + " start with Fitness: " + best.fitness);
- }
- for (Individual i : population) {
- i.fitness = evaluatePosition(i.position);
- if (moreInformation) {
- console.println("Fitness" + i.fitness);
- }
- if (i.fitness < best.fitness) {
- best = i;
- }
- }
- runList.add(best.fitness);
- List<Individual> childList = new ArrayList<Individual>();
- for (int k = 0; k < popsize / 2; k++) {
- Individual parentA = selectAParent(population, popsize);
- Individual parentB = selectAParent(population, popsize);
- Individual childA = new Individual(parentA);
- Individual childB = new Individual(parentB);
- crossover(childA, childB, problemSize);
- if (useIntervalMutation) {
- mutateInterval(childA, problemSize);
- } else {
- mutate(childA, problemSize);
- }
- if (useIntervalMutation) {
- mutateInterval(childB, problemSize);
- } else {
- mutate(childB, problemSize);
- }
- childList.add(childA);
- childList.add(childB);
- }
- population = childList;
- if (moreInformation) {
- console.println("________________");
- }
- if (cancel) {
- return null;
- }
- }
- console.println(" End with:" + best.fitness);
- this.runList = runList;
- return best;
- }
- /**
- * Algorithm 22 Bit-Flip Mutation.
- */
- private void mutate(Individual child, int problemSize) {
- ListIterator<Boolean> iter = child.position.listIterator();
- while (iter.hasNext()) {
- boolean boolValue = iter.next();
- if (Random.nextDouble() <= this.mutateProbability) {
- iter.set(!boolValue);
- }
- }
- }
- /**
- * Algorithm rolf
- */
- private void mutateInterval(Individual child, int problemSize) {
- //If not mutate skip
- if (Random.nextDouble() > this.mutateProbability) {
- return;
- }
- //println("problemSize:" + problemSize + " maxMutationPercent:" + maxMutationPercent);
- int maximumAmountOfMutatedBits = Math.max(1,
- (int) Math.round(((double) problemSize) * this.maxMutationPercent));
- int randomUniformAmountOfMutatedValues = Random.nextIntegerInRange(1,
- maximumAmountOfMutatedBits + 1);
- //println("max:" + maximumAmountOfMutatedBits + " actual:" + randomUniformAmountOfMutatedValues);
- TreeSet<Integer> mutationLocation = new TreeSet<Integer>(); //sortedSet
- //Choose the location to mutate
- for (int i = 0; i < randomUniformAmountOfMutatedValues; i++) {
- boolean success = mutationLocation.add(Random.nextIntegerInRange(0, problemSize));
- if (!success) {
- i--; //can be add up to some series long loops if maximumAmountOfMutatedBits get closed to problemsize.
- }
- }
- //println("Set:" + mutationLocation);
- ListIterator<Boolean> iter = child.position.listIterator();
- if (mutationLocation.isEmpty()) {
- return;
- }
- int firstindex = mutationLocation.pollFirst();
- while (iter.hasNext()) {
- int index = iter.nextIndex();
- boolean boolValue = iter.next();
- if (index == firstindex) {
- iter.set(!boolValue);
- if (mutationLocation.isEmpty()) {
- break;
- }
- firstindex = mutationLocation.pollFirst();
- }
- }
- }
- /**
- * Algorithm 25 Uniform Crossover. Probability is set to 1/Problemsize when not changed.
- */
- private void crossover(Individual childA, Individual childB, int problemSize) {
- ListIterator<Boolean> iterA = childA.position.listIterator();
- ListIterator<Boolean> iterB = childB.position.listIterator();
- for (int i = 0; i < problemSize; i++) {
- boolean boolA = iterA.next();
- boolean boolB = iterB.next();
- if (Random.nextDouble() <= this.swapProbability) {
- //Swap
- iterA.set(boolB);
- iterB.set(boolA);
- }
- }
- }
- /**
- * Algorithm 32 Tournament Selection. The fitnessValues are calculated for the Population List.
- * PseudoCode
- */
- private Individual selectAParent(List<Individual> population, int popsize) {
- Individual tournamentBest = population.get(Random.nextIntegerInRange(0, popsize));
- double participants;
- for (participants = tournamentSize; participants >= 2; participants -= 1.0) {
- Individual next = population.get(Random.nextIntegerInRange(0, popsize));
- if (next.fitness < tournamentBest.fitness) {
- tournamentBest = next;
- }
- }
- //if tournament size is not a whole number like 2.5 or 3.6
- //the remaining part is the chance to fight another time; 2.7 -> 70% chance to fight a second time
- if (participants > 1) {
- if (Random.nextDouble() < participants - 1.0) {
- //println("Chance to find a match");
- Individual next = population.get(Random.nextIntegerInRange(0, popsize));
- if (next.fitness < tournamentBest.fitness) {
- tournamentBest = next;
- }
- }
- }
- return tournamentBest;
- }
- /**
- * Initialize the Population with Individuals that have a random Position.
- */
- private List<Individual> initPopuluationRandom(int problemSize, Individual startIndidual) {
- List<Individual> population = new ArrayList<Individual>();
- for (int i = 0; i < popsize - 1; i++) {
- population.add(createRandomIndividualWithoutFitness(problemSize));
- }
- //Add Start Position
- population.add(new Individual(startIndidual));
- return population;
- }
- /**
- * Algorithm 21 The BooleanVeator initialization.
- *
- * @param problemSize
- * @return
- */
- private Individual createRandomIndividualWithoutFitness(int problemSize) {
- //create Random Individual Without Fitness
- Individual result = new Individual();
- result.position = new ArrayList<Boolean>();
- for (int index = 0; index < problemSize; index++) {
- result.position.add(Random.nextBoolean());
- }
- return result;
- }
- @Override
- protected String algoInformationToPrint() {
- // private int popsize = 20;
- // private int maxGenerations = 100;
- // private double tournamentSize = 2.0;
- // private double fixedSwapProbability = 0.02;
- // private boolean useFixedSpawProbability = false;
- // private double fixedMutateProbability = 0.02;
- // private boolean useFixedMutateProbability = false;
- // private boolean useIntervalMutation = true;
- // private double mutateProbabilityInterval = 0.01;
- // private double maxMutationPercent = 0.01;
- return "GaAlgo"
- + " Rounds: " + rounds
- + " Iterations: " + maxGenerations
- + " Individuals: " + popsize
- + " TournamentSize: " + tournamentSize
- + " SwapProbability: " + swapProbability
- + (useIntervalMutation ?
- (//" MutateProbabilityInterval: " + mutateProbabilityInterval
- " MaxMutationPercent: " + maxMutationPercent)
- :
- (" MutateProbability: " + mutateProbability));
- }
- @Override
- protected String plottFileName() {
- return "plottGaAlgo.txt";
- }
- }
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