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Commit 456e1b8

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chapter 5
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‎CCSPiJ/src/chapter5/Chromosome.java‎

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package chapter5;
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import java.util.List;
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public abstract class Chromosome<T extends Chromosome<T>> implements Comparable<T> {
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public abstract double fitness();
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public abstract List<T> crossover(T other);
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public abstract void mutate();
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public abstract T copy();
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@Override
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public int compareTo(T other) {
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Double mine = this.fitness();
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Double theirs = other.fitness();
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return mine.compareTo(theirs);
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}
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}
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package chapter5;
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import java.util.ArrayList;
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import java.util.Collections;
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import java.util.List;
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import java.util.Random;
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public class GeneticAlgorithm<C extends Chromosome<C>> {
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public enum SelectionType {
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ROULETTE, TOURNAMENT;
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}
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private ArrayList<C> population;
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private double mutationChance;
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private double crossoverChance;
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private SelectionType selectionType;
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private Random random;
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public GeneticAlgorithm(List<C> initialPopulation,
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double mutationChance, double crossoverChance, SelectionType selectionType) {
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this.population = new ArrayList<>(initialPopulation);
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this.mutationChance = mutationChance;
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this.crossoverChance = crossoverChance;
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this.selectionType = selectionType;
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this.random = new Random();
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}
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// Use the probability distribution wheel to pick numPicks individuals
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private List<C> pickRoulette(double[] wheel, int numPicks) {
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List<C> picks = new ArrayList<>();
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for (int i = 0; i < numPicks; i++) {
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double pick = random.nextDouble();
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for (int j = 0; j < wheel.length; j++) {
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pick -= wheel[j];
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if (pick <= 0) { // we had one that took us over, leads to a pick
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picks.add(population.get(j));
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break;
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}
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}
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}
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return picks;
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}
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// Pick a certain number of individuals via a tournament
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private List<C> pickTournament(int numParticipants, int numPicks) {
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// Find numParticipants random participants to be in the tournament
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Collections.shuffle(population);
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List<C> tournament = population.subList(0, numParticipants);
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// Find the numPicks highest fitnesses in the tournament
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Collections.sort(tournament, Collections.reverseOrder());
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return tournament.subList(0, numPicks);
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}
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// Replace the population with a new generation of individuals
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private void reproduceAndReplace() {
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ArrayList<C> nextPopulation = new ArrayList<>();
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// keep going until we've filled the new generation
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while (nextPopulation.size() < population.size()) {
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// pick the two parents
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List<C> parents;
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if (selectionType == SelectionType.ROULETTE) {
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// create the probability distribution wheel
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double totalFitness = population.stream().mapToDouble(C::fitness).sum();
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double[] wheel = population.stream()
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.mapToDouble(C -> C.fitness() / totalFitness).toArray();
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parents = pickRoulette(wheel, 2);
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} else { // tournament
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parents = pickTournament(population.size() / 2, 2);
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}
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// potentially crossover the 2 parents
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if (random.nextDouble() < crossoverChance) {
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C parent1 = parents.get(0);
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C parent2 = parents.get(1);
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nextPopulation.addAll(parent1.crossover(parent2));
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} else { // just add the two parents
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nextPopulation.addAll(parents);
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}
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}
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// if we have an odd number, we'll have 1 exra, so we remove it
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if (nextPopulation.size() > population.size()) {
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nextPopulation.remove(0);
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}
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population = nextPopulation; // replace the reference/generation
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}
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// With mutationChance probability, mutate each individual
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private void mutate() {
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for (C individual : population) {
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if (random.nextDouble() < mutationChance) {
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individual.mutate();
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}
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}
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}
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// Run the genetic algorithm for maxGenerations iterations
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// and return the best individual found
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public C run(int maxGenerations, double threshold) {
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C best = Collections.max(population).copy();
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for (int generation = 0; generation < maxGenerations; generation++) {
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// early exit if we beat threshold
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if (best.fitness() >= threshold) {
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return best;
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}
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// Debug printout
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System.out.println("Generation " + generation +
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" Best " + best.fitness() +
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" Avg " + population.stream()
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.mapToDouble(C::fitness).average().orElse(0.0));
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reproduceAndReplace();
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mutate();
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C highest = Collections.max(population);
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if (highest.fitness() > best.fitness()) {
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best = highest.copy();
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}
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}
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return best;
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}
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}
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package chapter5;
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import java.util.ArrayList;
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import java.util.Collections;
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import java.util.List;
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import java.util.Random;
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public class SendMoreMoney2 extends Chromosome<SendMoreMoney2> {
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private List<Character> letters;
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private Random random;
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public SendMoreMoney2(List<Character> letters) {
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this.letters = letters;
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random = new Random();
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}
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public static SendMoreMoney2 randomInstance() {
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List<Character> letters = new ArrayList<>(
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List.of('S', 'E', 'N', 'D', 'M', 'O', 'R', 'Y', ' ', ' '));
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Collections.shuffle(letters);
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return new SendMoreMoney2(letters);
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}
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@Override
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public double fitness() {
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int s = letters.indexOf('S');
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int e = letters.indexOf('E');
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int n = letters.indexOf('N');
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int d = letters.indexOf('D');
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int m = letters.indexOf('M');
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int o = letters.indexOf('O');
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int r = letters.indexOf('R');
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int y = letters.indexOf('Y');
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int send = s * 1000 + e * 100 + n * 10 + d;
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int more = m * 1000 + o * 100 + r * 10 + e;
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int money = m * 10000 + o * 1000 + n * 100 + e * 10 + y;
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int difference = Math.abs(money - (send + more));
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return 1.0 / (difference + 1.0);
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}
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@Override
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public List<SendMoreMoney2> crossover(SendMoreMoney2 other) {
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SendMoreMoney2 child1 = new SendMoreMoney2(new ArrayList<>(letters));
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SendMoreMoney2 child2 = new SendMoreMoney2(new ArrayList<>(other.letters));
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int idx1 = random.nextInt(letters.size());
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int idx2 = random.nextInt(other.letters.size());
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Character l1 = letters.get(idx1);
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Character l2 = other.letters.get(idx2);
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int idx3 = letters.indexOf(l2);
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int idx4 = other.letters.indexOf(l1);
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Collections.swap(child1.letters, idx1, idx3);
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Collections.swap(child2.letters, idx2, idx4);
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return List.of(child1, child2);
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}
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@Override
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public void mutate() {
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int idx1 = random.nextInt(letters.size());
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int idx2 = random.nextInt(letters.size());
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Collections.swap(letters, idx1, idx2);
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}
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@Override
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public SendMoreMoney2 copy() {
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return new SendMoreMoney2(new ArrayList<>(letters));
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}
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@Override
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public String toString() {
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int s = letters.indexOf('S');
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int e = letters.indexOf('E');
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int n = letters.indexOf('N');
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int d = letters.indexOf('D');
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int m = letters.indexOf('M');
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int o = letters.indexOf('O');
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int r = letters.indexOf('R');
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int y = letters.indexOf('Y');
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int send = s * 1000 + e * 100 + n * 10 + d;
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int more = m * 1000 + o * 100 + r * 10 + e;
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int money = m * 10000 + o * 1000 + n * 100 + e * 10 + y;
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int difference = Math.abs(money - (send + more));
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return (send + " + " + more + " = " + money + " Difference: " + difference);
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}
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public static void main(String[] args) {
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ArrayList<SendMoreMoney2> initialPopulation = new ArrayList<>();
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for (int i = 0; i < 1000; i++) {
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initialPopulation.add(SendMoreMoney2.randomInstance());
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}
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GeneticAlgorithm<SendMoreMoney2> ga = new GeneticAlgorithm<>(
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initialPopulation,
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0.2, 0.7, GeneticAlgorithm.SelectionType.ROULETTE);
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SendMoreMoney2 result = ga.run(1000, 1.0);
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System.out.println(result);
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}
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}
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package chapter5;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Random;
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public class SimpleEquation extends Chromosome<SimpleEquation> {
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private int x, y;
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public SimpleEquation(int x, int y) {
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this.x = x;
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this.y = y;
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}
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public static SimpleEquation randomInstance() {
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Random random = new Random();
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return new SimpleEquation(random.nextInt(100), random.nextInt(100));
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}
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// 6x - x^2 + 4y - y^2
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@Override
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public double fitness() {
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return 6 * x - x * x + 4 * y - y * y;
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}
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@Override
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public List<SimpleEquation> crossover(SimpleEquation other) {
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SimpleEquation child1 = new SimpleEquation(x, other.y);
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SimpleEquation child2 = new SimpleEquation(other.x, y);
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return List.of(child1, child2);
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}
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@Override
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public void mutate() {
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Random random = new Random();
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if (random.nextDouble() > 0.5) { // mutate x
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if (random.nextDouble() > 0.5) {
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x += 1;
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} else {
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x -= 1;
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}
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} else { // otherwise mutate y
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if (random.nextDouble() > 0.5) {
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y += 1;
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} else {
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y -= 1;
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}
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}
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}
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@Override
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public SimpleEquation copy() {
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return new SimpleEquation(x, y);
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}
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@Override
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public String toString() {
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return "X: " + x + " Y: " + y + " Fitness: " + fitness();
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}
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public static void main(String[] args) {
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ArrayList<SimpleEquation> initialPopulation = new ArrayList<>();
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for (int i = 0; i < 20; i++) {
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initialPopulation.add(SimpleEquation.randomInstance());
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}
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GeneticAlgorithm<SimpleEquation> ga = new GeneticAlgorithm<>(
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initialPopulation,
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0.1, 0.7, GeneticAlgorithm.SelectionType.TOURNAMENT);
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SimpleEquation result = ga.run(100, 13.0);
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System.out.println(result);
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}
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}

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