A simple implementation of a genetic algorithm

SimpleDemoGA.java

import java.util.Random;

/**
 *
 * @author Vijini
 */

//Main class
public class SimpleDemoGA {

    Population population = new Population();
    Individual fittest;
    Individual secondFittest;
    int generationCount = 0;

    public static void main(String[] args) {

        Random rn = new Random();

        SimpleDemoGA demo = new SimpleDemoGA();

        //Initialize population
        demo.population.initializePopulation(10);

        //Calculate fitness of each individual
        demo.population.calculateFitness();

        System.out.println("Generation: " + demo.generationCount + " Fittest: " + demo.population.fittest);

        //While population gets an individual with maximum fitness
        while (demo.population.fittest < 5) {
            ++demo.generationCount;

            //Do selection
            demo.selection();

            //Do crossover
            demo.crossover();

            //Do mutation under a random probability
            if (rn.nextInt()%7 < 5) {
                demo.mutation();
            }

            //Add fittest offspring to population
            demo.addFittestOffspring();

            //Calculate new fitness value
            demo.population.calculateFitness();

            System.out.println("Generation: " + demo.generationCount + " Fittest: " + demo.population.fittest);
        }

        System.out.println("\nSolution found in generation " + demo.generationCount);
        System.out.println("Fitness: "+demo.population.getFittest().fitness);
        System.out.print("Genes: ");
        for (int i = 0; i < 5; i++) {
            System.out.print(demo.population.getFittest().genes[i]);
        }

        System.out.println("");

    }

    //Selection
    void selection() {

        //Select the most fittest individual
        fittest = population.getFittest();

        //Select the second most fittest individual
        secondFittest = population.getSecondFittest();
    }

    //Crossover
    void crossover() {
        Random rn = new Random();

        //Select a random crossover point
        int crossOverPoint = rn.nextInt(population.individuals[0].geneLength);

        //Swap values among parents
        for (int i = 0; i < crossOverPoint; i++) {
            int temp = fittest.genes[i];
            fittest.genes[i] = secondFittest.genes[i];
            secondFittest.genes[i] = temp;

        }

    }

    //Mutation
    void mutation() {
        Random rn = new Random();

        //Select a random mutation point
        int mutationPoint = rn.nextInt(population.individuals[0].geneLength);

        //Flip values at the mutation point
        if (fittest.genes[mutationPoint] == 0) {
            fittest.genes[mutationPoint] = 1;
        } else {
            fittest.genes[mutationPoint] = 0;
        }

        mutationPoint = rn.nextInt(population.individuals[0].geneLength);

        if (secondFittest.genes[mutationPoint] == 0) {
            secondFittest.genes[mutationPoint] = 1;
        } else {
            secondFittest.genes[mutationPoint] = 0;
        }
    }

    //Get fittest offspring
    Individual getFittestOffspring() {
        if (fittest.fitness > secondFittest.fitness) {
            return fittest;
        }
        return secondFittest;
    }


    //Replace least fittest individual from most fittest offspring
    void addFittestOffspring() {

        //Update fitness values of offspring
        fittest.calcFitness();
        secondFittest.calcFitness();

        //Get index of least fit individual
        int leastFittestIndex = population.getLeastFittestIndex();

        //Replace least fittest individual from most fittest offspring
        population.individuals[leastFittestIndex] = getFittestOffspring();
    }

}


//Individual class
class Individual {

    int fitness = 0;
    int[] genes = new int[5];
    int geneLength = 5;

    public Individual() {
        Random rn = new Random();

        //Set genes randomly for each individual
        for (int i = 0; i < genes.length; i++) {
            genes[i] = Math.abs(rn.nextInt() % 2);
        }

        fitness = 0;
    }

    //Calculate fitness
    public void calcFitness() {

        fitness = 0;
        for (int i = 0; i < 5; i++) {
            if (genes[i] == 1) {
                ++fitness;
            }
        }
    }

}

//Population class
class Population {

    int popSize = 10;
    Individual[] individuals = new Individual[10];
    int fittest = 0;

    //Initialize population
    public void initializePopulation(int size) {
        for (int i = 0; i < individuals.length; i++) {
            individuals[i] = new Individual();
        }
    }

    //Get the fittest individual
    public Individual getFittest() {
        int maxFit = Integer.MIN_VALUE;
        int maxFitIndex = 0;
        for (int i = 0; i < individuals.length; i++) {
            if (maxFit <= individuals[i].fitness) {
                maxFit = individuals[i].fitness;
                maxFitIndex = i;
            }
        }
        fittest = individuals[maxFitIndex].fitness;
        return individuals[maxFitIndex];
    }

    //Get the second most fittest individual
    public Individual getSecondFittest() {
        int maxFit1 = 0;
        int maxFit2 = 0;
        for (int i = 0; i < individuals.length; i++) {
            if (individuals[i].fitness > individuals[maxFit1].fitness) {
                maxFit2 = maxFit1;
                maxFit1 = i;
            } else if (individuals[i].fitness > individuals[maxFit2].fitness) {
                maxFit2 = i;
            }
        }
        return individuals[maxFit2];
    }

    //Get index of least fittest individual
    public int getLeastFittestIndex() {
        int minFitVal = Integer.MAX_VALUE;
        int minFitIndex = 0;
        for (int i = 0; i < individuals.length; i++) {
            if (minFitVal >= individuals[i].fitness) {
                minFitVal = individuals[i].fitness;
                minFitIndex = i;
            }
        }
        return minFitIndex;
    }

    //Calculate fitness of each individual
    public void calculateFitness() {

        for (int i = 0; i < individuals.length; i++) {
            individuals[i].calcFitness();
        }
        getFittest();
    }

}

Hello everyone! Do you have that code in Matlab? I need the same result in Matlab. Please, help me. Thank you in advance

Hello! Thank you for explaining this algorithm in easy way.... What changes can we do to make it solve Multi-Objective Optimization Problems? Probably making it Multi-Objective GA(MOGA)?