Max Fitness stuck at local maxima in genetic algorithm implementation

Question

Having trouble with this code below. It is implementation of population evolution. In my case the max fitness is struck at a local maxima everytime and is unable to reach max possible value. Kindly suggest necessary edits and reason for the same.

Individual.java

package genetic.algorithm.project;

import java.util.Random;

public class Individual {

    public static int SIZE = 300;
    private int[] genes = new int[SIZE];
    private double fitnessValue = 0.0;


    // Getters and Setters
    public void setGene(int index,int gene){
        this.genes[index] = gene;
    }

    public int getGene(int index){
        return this.genes[index];
    }

    public void setFitnessValue(double fitness){
        this.fitnessValue = fitness;
    }

    public double getFitnessValue(){
        return this.fitnessValue;
    }

    //Function to generate a new individual with random set of genes
    public void generateIndividual(){
        Random rand = new Random();
        for(int i=0;i<SIZE;i++){
            this.setGene(i, rand.nextInt(2));
        }
    }

    //Mutation Function
    public void mutate(){
        Random rand = new Random();
        int index = rand.nextInt(SIZE);
        this.setGene(index, 1-this.getGene(index)); // Flipping value of gene 
    }

    //Function to set Fitness value of an individual
    public int evaluate(){

        int fitness = 0;
        for(int i=0; i<SIZE; ++i) {
            fitness += this.getGene(i);
        }
        this.setFitnessValue(fitness);
        return fitness;
    }

}

Population.java

import java.util.Random;

public class Population {

    final static int ELITISM = 1;
    final static int POP_SIZE = 200+ELITISM; //Population size + Elitism (1)
    final static int MAX_ITER = 2000;
    final static double MUTATION_RATE = 0.05;
    final static double CROSSOVER_RATE = 0.7;
    private static Random rand = new Random(); 

    private double totalFitness;
    private  Individual[] pop;

    //Constructor
    public Population(){
        pop = new Individual[POP_SIZE];
        //Initialising population
        for(int i=0;i<POP_SIZE;i++){
            pop[i] = new Individual();
            pop[i].generateIndividual();

        }
        this.evaluate();
    }

    //Storing new generation in population
    public void setPopulation(Individual[] newPop) {
        this.pop = newPop;
    }


    //Method to find total fitness of population
    public double evaluate(){
        this.totalFitness = 0.0;
        for (int i = 0; i < POP_SIZE; i++) {
            this.totalFitness +=  pop[i].evaluate();
        }


       return this.totalFitness;
    }


    //Getters
    public Individual getIndividual(int index) {
        return pop[index];
    }


    //Function to find fittest individual for elitism
    public Individual getFittest() {
        Individual fittest = pop[0];
        for (int i = 0; i < POP_SIZE; i++) {
            if (fittest.getFitnessValue() <= getIndividual(i).getFitnessValue()) {
                fittest = getIndividual(i);
            }
        }
        return fittest;
    }

    //CROSSOVER Function : Takes 2 individuals and returns 2 new individuals
    public static Individual[] crossover(Individual indiv1,Individual indiv2) {
        Individual[] newIndiv = new Individual[2];
        newIndiv[0] = new Individual();
        newIndiv[1] = new Individual();
        int randPoint = rand.nextInt(Individual.SIZE);
        int i;
        for (i=0; i<randPoint; ++i) {
            newIndiv[0].setGene(i, indiv1.getGene(i));
            newIndiv[1].setGene(i, indiv2.getGene(i));
        }
        for (; i<Individual.SIZE; ++i) {
            newIndiv[0].setGene(i, indiv2.getGene(i));
            newIndiv[1].setGene(i, indiv1.getGene(i));
        }

        return newIndiv;
    }

    //Roulette Wheel Selection Function
    public Individual rouletteWheelSelection() {

        double randNum = rand.nextDouble() * this.totalFitness;
        int idx;

        for (idx=0; idx<POP_SIZE && randNum>0; idx++) {
            randNum -= pop[idx].getFitnessValue();
        }
        return pop[idx-1];
    }

    //Main method
    public static void main(String[] args) {
        Population pop = new Population();
        Individual[] newPop = new Individual[POP_SIZE];
        Individual[] indiv = new Individual[2];
        //Current Population Stats
        System.out.println("Total Fitness = "+pop.totalFitness);
        System.out.println("Best  Fitness = "+pop.getFittest().getFitnessValue());

        int count;
        for(int iter=0;iter<MAX_ITER;iter++){
            count =0;

                //Elitism
                newPop[count] = pop.getFittest();
                count++;

           //Creating new population
            while(count < POP_SIZE){
                //Selecting parents
                indiv[0] = pop.rouletteWheelSelection();
                indiv[1] = pop.rouletteWheelSelection();

                // Crossover
                if (rand.nextDouble() < CROSSOVER_RATE ) {
                    indiv = crossover(indiv[0], indiv[1]);
                }

                // Mutation
                if ( rand.nextDouble() < MUTATION_RATE ) {
                    indiv[0].mutate();
                }
                if ( rand.nextDouble() < MUTATION_RATE ) {
                    indiv[1].mutate();
                }

                // add to new population
                newPop[count] = indiv[0];
                newPop[count+1] = indiv[1];
                count += 2;
            }
            // Saving new population in pop
            pop.setPopulation(newPop);
            //Evaluating new population
            pop.evaluate();
            System.out.print("Total Fitness = " + pop.totalFitness);
            System.out.println(" ; Best Fitness = " +pop.getFittest().getFitnessValue()); 

        }
        Individual bestIndiv = pop.getFittest();
    }

}

The max possible value of fitness is 300 in my case but it always stucks around 200-230.

Answer 1

Replaced this function :

public void setPopulation(Individual[] newPop) {
    this.pop = newPop;
}

with

public void setPopulation(Individual[] newPop) {
    System.arraycopy(newPop, 0, this.pop, 0, POP_SIZE);
}

and it works fine now.