import java.util.Random; public class GA { int PopulationSize; int iter; int numBits; double crossoverRate; double mutationRate; int maxIntValue = 1023; int minIntValue = 0; public GA(int PopulationSize, int iter, int numBits, double crossoverRate, double mutationRate) { this.PopulationSize = PopulationSize; this.iter = iter; this.numBits = numBits; this.crossoverRate = crossoverRate; this.mutationRate = mutationRate; } public void runGA() { //encoding and initialized the first population int[][] Population = Encoding(PopulationSize, numBits); for (int i = 0; i < iter; i++) { double[] fitness = fitnessEvaluation(Population); int[][] newPopulation = new int[PopulationSize][numBits]; for (int j = 0; j < PopulationSize; j += 2) { int[][] parents = Selection(Population, fitness); int[][] children; if (Math.random() < crossoverRate) { children = Crossover(parents); } else { children = parents; } for (int k = 0; k < 2; k++) { if (Math.random() < mutationRate) { children[k] = Mutation(children[k]); } newPopulation[j + k] = children[k]; } } Population = newPopulation; } double[] fitness = fitnessEvaluation(Population); display(Population); findBest(fitness, Population); } /** * Encode and initialize the first population * * @param n * @param numBits * @return */ private int[][] Encoding(int n, int numBits) { int[][] Population = new int[n][numBits]; for (int i = 0; i < n; i++) for (int j = 0; j < numBits; j++) { double x = Math.random(); if (x < .5) Population[i][j] = 0; else { Population[i][j] = 1; } } return Population; } /** * Calculate the fitness value of each individual */ private double[] fitnessEvaluation(int[][] Population) { double[] fitness = new double[Population.length]; for (int i = 0; i < Population.length; i++) { int x = decode(Population[i]); fitness[i] = 1023 * x - x * x; } return fitness; } /** * Use the Roulette Wheel selection Remember that the sum of the fitness ratio * is equal to 1. */ /** * Selects two individuals from the population using fitness-proportional selection. * @param Population the current population of individuals * @param fitness an array of fitness values for each individual in the population * @return a 2D array containing the selected parents */ private int[][] Selection(int[][] Population, double[] fitness) { // create a 2D array to hold the selected parents int[][] Parents = new int[2][numBits]; // calculate the total fitness of the population double totalFitness = 0; for (int i = 0; i < fitness.length; i++) { totalFitness += fitness[i]; } // create a random number generator Random rand = new Random(); // generate a random number between 0 and the total fitness of the population double r = rand.nextDouble() * totalFitness; // initialize a variable to keep track of the partial sum of fitness values double partialSum = 0; // initialize a variable to keep track of the index of the selected individual int selected = -1; // loop through the fitness values, adding them to the partial sum and checking if it excee.

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