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A simple implementation of a genetic algorithm
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();
}
}
@liangyihuai
Copy link

Hello, I read blog in the page (link) and understand your code. I find a big bug in the source code and finally I fix it. Before fixing, your source code sometime does not converge. The reason is because you change the genes of parent. So I add function "clone()" to make children and parent refer to different objects. The following is my fixed source code.

package com.huai.genetic_algo;

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 implements Cloneable{

    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;
            }
        }
    }


    @Override
    protected Object clone() throws CloneNotSupportedException {
        Individual individual = (Individual)super.clone();
        individual.genes = new int[5];
        for(int i = 0; i < individual.genes.length; i++){
            individual.genes[i] = this.genes[i];
        }
        return individual;
    }
}

//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;
        try {
            return (Individual) individuals[maxFitIndex].clone();
        } catch (CloneNotSupportedException e) {
            e.printStackTrace();
        }
        return null;
    }

    //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;
            }
        }
        try {
            return (Individual) individuals[maxFit2].clone();
        } catch (CloneNotSupportedException e) {
            e.printStackTrace();
        }
        return null;
    }

    //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();
    }

}

@memento
Copy link

memento commented Nov 14, 2019

Hello, I read blog in the page (link) and understand your code. I find a big bug in the source code and finally I fix it. Before fixing, your source code sometime does not converge. The reason is because you change the genes of parent. So I add function "clone()" to make children and parent refer to different objects. The following is my fixed source code.

package com.huai.genetic_algo;

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 implements Cloneable{

    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;
            }
        }
    }


    @Override
    protected Object clone() throws CloneNotSupportedException {
        Individual individual = (Individual)super.clone();
        individual.genes = new int[5];
        for(int i = 0; i < individual.genes.length; i++){
            individual.genes[i] = this.genes[i];
        }
        return individual;
    }
}

//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;
        try {
            return (Individual) individuals[maxFitIndex].clone();
        } catch (CloneNotSupportedException e) {
            e.printStackTrace();
        }
        return null;
    }

    //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;
            }
        }
        try {
            return (Individual) individuals[maxFit2].clone();
        } catch (CloneNotSupportedException e) {
            e.printStackTrace();
        }
        return null;
    }

    //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();
    }

}

Go check the repo :)
https://github.com/memento/GeneticAlgorithm

@Vini2
Copy link
Author

Vini2 commented Nov 14, 2019

Hello, I read blog in the page (link) and understand your code. I find a big bug in the source code and finally I fix it. Before fixing, your source code sometime does not converge. The reason is because you change the genes of parent. So I add function "clone()" to make children and parent refer to different objects. The following is my fixed source code.

package com.huai.genetic_algo;

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 implements Cloneable{

    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;
            }
        }
    }


    @Override
    protected Object clone() throws CloneNotSupportedException {
        Individual individual = (Individual)super.clone();
        individual.genes = new int[5];
        for(int i = 0; i < individual.genes.length; i++){
            individual.genes[i] = this.genes[i];
        }
        return individual;
    }
}

//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;
        try {
            return (Individual) individuals[maxFitIndex].clone();
        } catch (CloneNotSupportedException e) {
            e.printStackTrace();
        }
        return null;
    }

    //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;
            }
        }
        try {
            return (Individual) individuals[maxFit2].clone();
        } catch (CloneNotSupportedException e) {
            e.printStackTrace();
        }
        return null;
    }

    //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();
    }

}

Thank you very much for the fix @liangyihuai

@Vini2
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Vini2 commented Nov 14, 2019

Hello, I read blog in the page (link) and understand your code. I find a big bug in the source code and finally I fix it. Before fixing, your source code sometime does not converge. The reason is because you change the genes of parent. So I add function "clone()" to make children and parent refer to different objects. The following is my fixed source code.

package com.huai.genetic_algo;

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 implements Cloneable{

    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;
            }
        }
    }


    @Override
    protected Object clone() throws CloneNotSupportedException {
        Individual individual = (Individual)super.clone();
        individual.genes = new int[5];
        for(int i = 0; i < individual.genes.length; i++){
            individual.genes[i] = this.genes[i];
        }
        return individual;
    }
}

//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;
        try {
            return (Individual) individuals[maxFitIndex].clone();
        } catch (CloneNotSupportedException e) {
            e.printStackTrace();
        }
        return null;
    }

    //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;
            }
        }
        try {
            return (Individual) individuals[maxFit2].clone();
        } catch (CloneNotSupportedException e) {
            e.printStackTrace();
        }
        return null;
    }

    //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();
    }

}

Go check the repo :)

Thank you very much for sharing this with me @memento. This looks very nice with the visualizations.

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

@baptiste2k8
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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)?

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