Develop Reference

How to correctly implement maximum weight independent set of positive tree using BFS

can someone help me implement the maximum weight independent set for a TREE (not a graph)?
The tree is represented by an adjacency matrix, and we have an array for the weights of the vertices.
BFS output: // 0: distances from start vertex
// 1: BFS-order
// 2: parent-IDs
I tried this code, it doesn't work on all test cases and it says most of the time that the weight is too small.
Can someone help me find the errors?
import java.io.*;
import java.util.*;
public class Lab5
{
/**
* Problem: Find a maximum weight independent set using dynammic programming.
*/
private static int[] problem(Tree t, int[] weights)
{
// Implement me!
//base cases
if (t.noOfVertices==0) {
return new int[] {};
}
if (t.noOfVertices==1) {
return new int[] {weights[0]};
}
//we will implement this using bfs, we will use 0 as the root
int[][] bfs = t.bfs(0);
//finding leaves
int leaf[] = new int [t.noOfVertices];
//now we can implement our algorithm
//M is the maximum weight of the tree if it contains i, and M1 is the maximum weight of the tree if it doesn't contain i
int M[]=new int[t.noOfVertices];
int M1[]=new int[t.noOfVertices];
//treating elements that aren't leaves
int nodeDiscovered[] = new int[t.noOfVertices];
for (int i = 0; i<t.noOfVertices; i++) {
if (t.edges[i].length==1) {
leaf[i]=1;
M[i]=weights[i];
nodeDiscovered[i]=1;
M1[i]=0;
}
else {
leaf[i]=0;
nodeDiscovered[i]=0;
}
}
for (int i = 1; i<t.noOfVertices; i++) {
if (leaf[i]==1) {
int node = bfs[2][i];
if (nodeDiscovered[node]!=0) {
continue;
}
while (node>-1) {
int parent = bfs[2][node];
ArrayList<Integer> sibs = new ArrayList<Integer>();
if (parent!=-1) {
for (int j = 0; j<t.edges[parent].length; j++) {
if (t.edges[parent][j]!=bfs[2][parent]) {
sibs.add(t.edges[parent][j]);
}
}
}
else {
sibs.add(node);
}
for (int sib : sibs) {
if (nodeDiscovered[sib]!=0) {
continue;
}
M[sib]=weights[sib];
for (int k : t.edges[sib]) {
if(bfs[0][sib]==bfs[0][k]-1) {
M[sib]=M[sib]+M1[k];
M1[sib]+=(M[k]>M1[k])?M[k]:M1[k];
}
}
nodeDiscovered[sib]=1;
}
node = bfs[2][node];
}
}
}
//putting the answers in an arraylist
ArrayList<Integer> set = new ArrayList<Integer>();
if (M[0]>M1[0]) {
set.add(0);
}
for (int i = 1; i<t.noOfVertices; i++) {
if (!set.contains(bfs[2][i]) && M[i]>=M1[i] ) {
set.add(i);
}
}
System.out.println(set);
//putting the elements of the arraylist into an array of int
int[] set1 = new int[set.size()];
for (int i = 0; i<set.size(); i++) {
set1[i]=set.get(i);
}
return set1;
}
// ---------------------------------------------------------------------
// Do not change any of the code below!
// Do not change any of the code below!
/**
* Determines if a given set of vertices is an independent set for the given tree.
*/
private static boolean isIndSet(Tree t, int[] set)
{
if (set == null) return false;
boolean[] covered = new boolean[t.noOfVertices];
for (int i = 0; i < set.length; i++)
{
int vId = set[i];
int[] neighs = t.edges[vId];
if (covered[vId]) return false;
covered[vId] = true;
for (int j = 0; j < neighs.length; j++)
{
int nId = neighs[j];
covered[nId] = true;
}
}
return true;
}
private static final int LabNo = 5;
private static final String course = "CS 427";
private static final String quarter = "Fall 2021";
private static final Random rng = new Random(190817);
private static boolean testProblem(int[][] testCase)
{
int[] parents = testCase[0];
int[] weights = testCase[1];
Tree t = Tree.fromParents(parents);
int[] solution = maxIsWeight(t, weights);
int isWeight = solution[0];
int isSize = solution[1];
int[] answer = problem(t, weights.clone());
if (!isIndSet(t, answer))
{
System.out.println("Not an independent set.");
return false;
}
int ansWeight = 0;
for (int i = 0; i < answer.length; i++)
{
ansWeight += weights[answer[i]];
}
if (ansWeight < isWeight)
{
System.out.println("Weight too small.");
return false;
}
if (answer.length < isSize)
{
System.out.println("Set too small.");
return false;
}
return true;
}
private static int[] maxIsWeight(Tree t, int[] weigh)
{
int n = t.noOfVertices;
int[][] dfs = t.dfs(0);
int[] post = dfs[2];
int[] w = new int[n];
for (int i = 0; i < n; i++)
{
w[i] = weigh[i] * n + 1;
}
boolean[] isCandidate = new boolean[n];
for (int i = 0; i < n; i++)
{
int vId = post[i];
if (w[vId] <= 0) continue;
isCandidate[vId] = true;
int[] neighs = t.edges[vId];
for (int j = 0; j < neighs.length; j++)
{
int uId = neighs[j];
w[uId] = Math.max(w[uId] - w[vId], 0);
}
}
int isWeight = 0;
int isSize = 0;
for (int i = n - 1; i >= 0; i--)
{
int vId = post[i];
if (!isCandidate[vId]) continue;
isWeight += weigh[vId];
isSize++;
int[] neighs = t.edges[vId];
for (int j = 0; j < neighs.length; j++)
{
int uId = neighs[j];
isCandidate[uId] = false;
}
}
return new int[] { isWeight, isSize };
}
public static void main(String args[])
{
System.out.println(course + " -- " + quarter + " -- Lab " + LabNo);
int noOfTests = 300;
boolean passedAll = true;
System.out.println("-- -- -- -- --");
System.out.println(noOfTests + " random test cases.");
for (int i = 1; i <= noOfTests; i++)
{
boolean passed = false;
boolean exce = false;
try
{
int[][] testCase = createProblem(i);
passed = testProblem(testCase);
}
catch (Exception ex)
{
passed = false;
exce = true;
ex.printStackTrace();
}
if (!passed)
{
System.out.println("Test " + i + " failed!" + (exce ? " (Exception)" : ""));
passedAll = false;
//break;
}
}
if (passedAll)
{
System.out.println("All test passed.");
}
}
private static int[][] createProblem(int testNo)
{
int size = rng.nextInt(Math.min(testNo, 5000)) + 5;
// -- Generate tree. ---
int[] parents = new int[size];
parents[0] = -1;
for (int i = 1; i < parents.length; i++)
{
parents[i] = rng.nextInt(i);
}
// -- Generate weights. ---
int[] weights = new int[size];
for (int i = 0; i < weights.length; i++)
{
weights[i] = rng.nextInt(256);
}
return new int[][] { parents, weights };
}
}
I attached an image that contains the algorithm that I used.

Max Fitness stuck at local maxima in genetic algorithm implementation

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.

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.

Simple prisoner's dilemma genetic algorithm

I have used an existing genetic algorithm from
here
and reworked it I but don't know what I'm doing wrong
This is the error that I get
Exception in thread "main" java.lang.NullPointerException at
simpleGa.Algorithm.crossover(Algorithm.java:69) at
simpleGa.Algorithm.evolvePopulation(Algorithm.java:34) at
simpleGa.GAprisonerdilemma.main(GAprisonerdilemma.java:41)
I can't figure out exactly where the mistake is. Read a lot about NullPointerException but couldn't figure it out
package simpleGa;
public class Population {
public static Individual[] individuals;
/*
* Constructors
*/
// Create a population
public Population(int populationSize, boolean initialise) {
individuals = new Individual[populationSize];
// Initialise population
if (initialise) {
// Loop and create individuals
for (int i = 0; i < size(); i++) {
Individual newIndividual = new Individual();
newIndividual.generateIndividual();
saveIndividual(i, newIndividual);
}
for(int i=0;i<size();i++)
{
if(i%2==1){Individual individual1=individuals[i-1];
Individual individual2=individuals[i];
if(individuals[i-1].getGene(i-1)==0 && individuals[i].getGene(i)==0){
individuals[i-1].fitness=individual1.fitness+1;
individuals[i].fitness=individual2.fitness+1;
}
if(individuals[i-1].getGene(i-1)==1 && individuals[i].getGene(i)==1){
individuals[i-1].fitness=individual1.fitness+2;
individuals[i].fitness=individual2.fitness+2;
}
if(individuals[i-1].getGene(i-1)==0 && individuals[i].getGene(i)==1){
individuals[i-1].fitness=individual1.fitness+3;
individuals[i].fitness=individual2.fitness+0;
}
if(individuals[i-1].getGene(i-1)==1 && individuals[i].getGene(i)==0){
individuals[i-1].fitness=individual1.fitness+0;
individuals[i].fitness=individual2.fitness+3;
}
}}}
}
/* Getters */
public Individual getIndividual(int index) {
return individuals[index];
}
public Individual getFittest() {
Individual fittest = individuals[0];
// Loop through individuals to find fittest
for (int i = 1; i < size(); i++) {
if (fittest.getFitness() <= getIndividual(i).getFitness()) {
fittest = getIndividual(i);
}
}
return fittest;
}
/* Public methods */
// Get population size
public int size() {
return individuals.length;
}
// Save individual
public void saveIndividual(int index, Individual indiv) {
individuals[index] = indiv;
}
}
package simpleGa;
public class Individual {
static int defaultGeneLength = 1000;
private long[] genes =new long [defaultGeneLength];
// Cache
public static int fitness = 0;
// Create a random individual
public void generateIndividual() {
for (int i = 0; i < size(); i++) {
long gene = Math.round(Math.random());
genes[i] = gene;
}
}
/* Getters and setters */
// Use this if you want to create individuals with different gene lengths
public static void setDefaultGeneLength(int length) {
defaultGeneLength = length;
}
public long getGene(int i) {
return genes[i];
}
public void setGene(int index, long value) {
genes[index] = value;
fitness = 0;
}
/* Public methods */
public int size() {
return genes.length;
}
public static int getFitness() {
return fitness;
}
public void setFitness(int i) {
fitness=i;
}
#Override
public String toString() {
String geneString = "";
for (int i = 0; i < size(); i++) {
geneString += getGene(i);
}
return geneString;
}
}
package simpleGa;
public class Algorithm {
/* GA parameters */
private static final double uniformRate = 0.5;
private static final double mutationRate = 0.015;
private static final int tournamentSize = 5;
private static final boolean elitism = true;
/* Public methods */
// Evolve a population
public static Population evolvePopulation(Population pop) {
Population newPopulation = new Population(pop.size(), false);
// Keep our best individual
if (elitism) {
newPopulation.saveIndividual(0, pop.getFittest());
}
// Crossover population
int elitismOffset;
if (elitism) {
elitismOffset = 1;
} else {
elitismOffset = 0;
}
// Loop over the population size and create new individuals with
// crossover
for (int i = elitismOffset; i < pop.size(); i++) {
Individual indiv1 = tournamentSelection(pop);
Individual indiv2 = tournamentSelection(pop);
Individual newIndiv = crossover(indiv1, indiv2);
newPopulation.saveIndividual(i, newIndiv);
}
// Mutate population
for (int i = elitismOffset; i < newPopulation.size(); i++) {
mutate(newPopulation.getIndividual(i));
}
for(int i=0;i<pop.size();i++)
{for(int j=0;j<pop.getIndividual(i).size();j++)
{if(i%2==1){Individual individual1=Population.individuals[i-1];
Individual individual2=Population.individuals[i];
if(Population.individuals[i-1].getGene(i-1)==0 && Population.individuals[i].getGene(i)==0){
Population.individuals[i-1].fitness=individual1.fitness+1;
Population.individuals[i].fitness=individual2.fitness+1;
}
if(Population.individuals[i-1].getGene(i-1)==1 && Population.individuals[i].getGene(i)==1){
Population.individuals[i-1].fitness=individual1.fitness+2;
Population.individuals[i].fitness=individual2.fitness+2;
}
if(Population.individuals[i-1].getGene(i-1)==0 && Population.individuals[i].getGene(i)==1){
Population.individuals[i-1].fitness=individual1.fitness+3;
Population.individuals[i].fitness=individual2.fitness+0;
}
if(Population.individuals[i-1].getGene(i-1)==1 && Population.individuals[i].getGene(i)==0){
Population.individuals[i-1].fitness=individual1.fitness+0;
Population.individuals[i].fitness=individual2.fitness+3;
} }}}``
return newPopulation;
}
// Crossover individuals
private static Individual crossover(Individual indiv1, Individual indiv2) {
Individual newSol = new Individual();
// Loop through genes
for (int i = 0; i < indiv1.size(); i++) {
// Crossover
if (Math.random() <= uniformRate) {
newSol.setGene(i, indiv1.getGene(i));
} else {
newSol.setGene(i, indiv2.getGene(i));
}
}
return newSol;
}
// Mutate an individual
private static void mutate(Individual indiv) {
// Loop through genes
for (int i = 0; i < indiv.size(); i++) {
if (Math.random() <= mutationRate) {
// Create random gene
long gene = Math.round(Math.random());
indiv.setGene(i, gene);
}
}
}
// Select individuals for crossover
private static Individual tournamentSelection(Population pop) {
// Create a tournament population
Population tournament = new Population(tournamentSize, false);
// For each place in the tournament get a random individual
for (int i = 0; i < tournamentSize; i++) {
int randomId = (int) (Math.random() * pop.size());
tournament.saveIndividual(i, pop.getIndividual(randomId));
}
// Get the fittest
Individual fittest = tournament.getFittest();
return fittest;
}
package simpleGa;
public class FitnessCalc {
/* Public methods */
// Set a candidate solution as a byte array
// To make it easier we can use this method to set our candidate solution
// with string of 0s and 1s
// Calculate inidividuals fittness by comparing it to our candidate solution
static int getFitness(Individual individual) {
int fitness = 0;
// Loop through our individuals genes and compare them to our cadidates
fitness=Individual.fitness;
return fitness;
}
}
// Get optimum fitness
}
package simpleGa;
import java.util.Scanner;
public class GAprisonerdilemma {
public static void main(String[] args) {
// Set a candidate solution
Scanner keyboard = new Scanner(System.in);
System.out.println("Input number of games!");
int k = keyboard.nextInt();
Individual.setDefaultGeneLength(k);
// Create an initial population
System.out.println("Input number of individuals in the population!");
int p = keyboard.nextInt();
Population myPop = new Population(p, true);
System.out.println("Input acceptable number of generations!");
int l = keyboard.nextInt();
// Evolve our population until we reach an optimum solution
int generationCount = 0;
int j=l+1;
System.out.println("Input requiered fitness value !");
int f = keyboard.nextInt();
int h=0;
// Evolve our population until we reach an optimum solution
for(int i=0;i<j;i++)
{
if(i==0){}
else{
if(myPop.getFittest().getFitness()>=f){if(h==0){h++;}
else{ System.out.println("Solution found!");
System.out.println("Generation: " + generationCount);
System.out.println( "Fitness(Points): " + myPop.getFittest().getFitness());
break;}
}else {myPop = Algorithm.evolvePopulation(myPop);
generationCount++;
System.out.println("Generation: " + generationCount + " Fittest: " + myPop.getFittest().getFitness());
}
if(i==j-1){ if(myPop.getFittest().getFitness()>=f)System.out.println("Solution found !");
else System.out.println("Solution not found closest solution is!");
System.out.println("Generation: " + generationCount);
System.out.println( " Fitness(Points): " + myPop.getFittest().getFitness());}
}
}
System.out.println("0 for betrays in that turn 1 for cooperates!");
System.out.println("Turns:");
System.out.println(myPop.getFittest());
}
}

Can't locate the problems in this simple Genetic Algorithm program

I have written a simple genetic algorithm program in java. What it is does is maximize the decimal value represented by the bits in the chromosome. Somehow mutation is not working as expected, e.g. causing two genes to mutate when just one is to change. The print statements I have included there show which to mutate, but in addition to that some more chromosomes get mutated. I can't figure out what the problem is :-(
Here are my java classes.
Gene.java
public class Gene {
private int value;
public Gene() {
value = Math.random() < 0.5 ? 0 : 1;
}
public Gene(int value) {
if (value != 0 && value != 1) {
throw new IllegalArgumentException("value must be either 0 or 1");
}
else {
this.value = value;
}
}
public void mutate() {
value = 1 - value;
}
public int value() {
return value;
}
#Override
public String toString() {
return String.valueOf(value);
}
}
Chromosome.java
import java.util.ArrayList;
import java.util.List;
public class Chromosome implements Comparable {
private ArrayList<Gene> genes;
private final int chromosomeLength;
public Chromosome(int length) {
this.genes = new ArrayList<>();
this.chromosomeLength = length > 0 ? length : 16;
for (int i = 0; i < chromosomeLength; i++) {
this.genes.add(i, new Gene());
}
}
public List<Gene> getAllele(int fromIndex, int toIndex) {
return new ArrayList<>(genes.subList(fromIndex, toIndex));
}
public void setAllele(int fromIndex, List<Gene> allele) {
int lastIndex = fromIndex + allele.size();
if (lastIndex > chromosomeLength) {
throw new IndexOutOfBoundsException("the allele exceeds beyond the size of the chromosome");
}
for (int i = fromIndex, j = 0; i < lastIndex; i++, j++) {
genes.set(i, allele.get(j));
}
}
public int getChromosomeLength() {
return chromosomeLength;
}
public void setGeneAt(int index, Gene gene) {
genes.set(index, gene);
}
public Gene getGeneAt(int index) {
return genes.get(index);
}
public int value() {
return Integer.parseInt(this.toString(), 2);
}
#Override
public String toString() {
StringBuilder chromosome = new StringBuilder("");
genes.stream().forEach((Gene g) -> chromosome.append(g));
return chromosome.toString();
}
#Override
public int compareTo(Object anotherChromosome) {
Chromosome c = (Chromosome) anotherChromosome;
return this.value() - c.value();
}
}
GenePool.java
import java.util.ArrayList;
import java.util.Arrays;
public class GenePool {
private final ArrayList<Chromosome> genePool;
private final int genePoolSize;
private final int chromosomeLength;
private final double crossOverRate;
private final double mutationRate;
private int[] crossPoints;
public GenePool(int numOfChromosome, int chromosomeLength, double crossOverRate, double mutationRate) {
this.genePoolSize = numOfChromosome;
this.chromosomeLength = chromosomeLength > 0 ? chromosomeLength : 16;
this.crossOverRate = crossOverRate;
this.mutationRate = mutationRate;
crossPoints = new int[1];
crossPoints[0] = this.chromosomeLength / 2;
genePool = new ArrayList<>();
for (int i = 0; i < numOfChromosome; i++) {
genePool.add(new Chromosome(chromosomeLength));
}
}
public int getGenePoolSize() {
return genePoolSize;
}
public Chromosome getChromosomeAt(int index) {
return genePool.get(index);
}
public void setChromosomeAt(int index, Chromosome c) {
genePool.set(index, c);
}
public int getChromosomeLength() {
return chromosomeLength;
}
public Chromosome[] crossOver(Chromosome c1, Chromosome c2) {
Chromosome[] offsprings = new Chromosome[2];
offsprings[0] = new Chromosome(c1.getChromosomeLength());
offsprings[1] = new Chromosome(c1.getChromosomeLength());
Chromosome[] parentChromosomes = {c1, c2};
int selector = 0;
for (int i = 0, start = 0; i <= crossPoints.length; i++) {
int crossPoint = i == crossPoints.length ? c1.getChromosomeLength() : crossPoints[i];
offsprings[0].setAllele(start, parentChromosomes[selector].getAllele(start, crossPoint));
offsprings[1].setAllele(start, parentChromosomes[1 - selector].getAllele(start, crossPoint));
selector = 1 - selector;
start = crossPoint;
}
return offsprings;
}
public void mutateGenePool() {
int totalGeneCount = genePoolSize * chromosomeLength;
System.out.println("Mutating genes:");
for (int i = 0; i < totalGeneCount; i++) {
double prob = Math.random();
if (prob < mutationRate) {
System.out.printf("Chromosome#: %d\tGene#: %d\n", i / chromosomeLength, i % chromosomeLength);
genePool.get(i / chromosomeLength).getGeneAt(i % chromosomeLength).mutate();
}
}
System.out.println("");
}
public int getLeastFitIndex() {
int index = 0;
int min = genePool.get(index).value();
int currentValue;
for (int i = 1; i < genePoolSize; i++) {
currentValue = genePool.get(i).value();
if (currentValue < min) {
index = i;
min = currentValue;
}
}
return index;
}
public void saveFittest(ArrayList<Chromosome> offsprings) {
// sort in ascending order
offsprings.sort(null);
offsprings.stream().forEach((offspring) -> {
int leastFitIndex = getLeastFitIndex();
if (offspring.value() > genePool.get(leastFitIndex).value()) {
genePool.set(leastFitIndex, offspring);
}
});
}
public void evolve(int noOfGeneration) {
for (int generation = 1; generation <= noOfGeneration; generation++) {
System.out.println("Generation :" + generation);
ArrayList<Integer> selection = new ArrayList<>();
for (int i = 0; i < genePoolSize; i++) {
if (Math.random() <= crossOverRate) {
selection.add(i);
}
}
if (selection.size() % 2 == 1) {
selection.remove(selection.size() - 1);
}
ArrayList<Chromosome> offsprings = new ArrayList<>();
for (int i = 0; i < selection.size(); i += 2) {
int index1 = selection.get(i);
int index2 = selection.get(i + 1);
offsprings.addAll(Arrays.asList(crossOver(genePool.get(index1), genePool.get(index2))));
}
System.out.println("Before saving the offsprings");
displayChromosomes(genePool, "GenePool");
displayChromosomes(offsprings, "Offsprings");
saveFittest(offsprings);
System.out.println("Before mutation:");
displayChromosomes(genePool, "GenePool");
mutateGenePool();
System.out.println("After mutation:");
displayChromosomes(genePool, "GenePool");
System.out.println("\n\n");
}
}
public void displayChromosomes(ArrayList<Chromosome> geneList, String name) {
System.out.println(name);
if (geneList.isEmpty()) {
System.out.println("Empty list");
}
geneList.stream().forEach((c) -> {
System.out.println(c + " -> " + c.value());
});
System.out.println("");
}
}
GADemo.java
public class GADemo {
public static void main(String[] args) {
GenePool gp = new GenePool(6, 8, 0.25, 0.01);
gp.evolve(10);
}
}
After evolving for a number of generations, the chromosomes all tend to become exactly the same, or very similar. And the problem is that that value is not the maximum for that many bits, and sometimes even a small value. For example, for 8 bits the values should (tend to) approach 255, but this doesn't do so in my code. Someone please provide a hint where/how to look for and solve the problem.

Focus on these lines and imagine the references. These are from setAllele()
for (int i = fromIndex, j = 0; i < lastIndex; i++, j++) {
genes.set(i, allele.get(j));
}
You are basically copying the reference from one onto the other. They are the same Gene so whatever mutation you do on those genes, will also affect even other Chromosomes.
You must produce a deep copy here.

Initially each chromosome has an own list of genes. But when you do the crossover operation you set gene objects from one chromosome into the gene list of other chromosome.
When you evolve the system, the number of shared genes will rise and therefore ultimately all chromosomes will share the same genes. No matter how you mutate a gene the chromosomes are not affected.
EDIT:
As Incognito also answered the setAllele method seems to be the culprit where gene sharing starts. You may want to introduce a method in the gene class where you can set its value given another gene.

Mean, Median, and Mode - Newb - Java

We had a lab in Comsci I couldn't figure out. I did a lot of research on this site and others for help but they were over my head. What threw me off were the arrays. Anyway, thanks in advance. I already got my grade, just want to know how to do this :D
PS: I got mean, I just couldn't find the even numbered median and by mode I just gave up.
import java.util.Arrays;
import java.util.Random;
public class TextLab06st
{
public static void main(String args[])
{
System.out.println("\nTextLab06\n");
System.out.print("Enter the quantity of random numbers ===>> ");
int listSize = Expo.enterInt();
System.out.println();
Statistics intList = new Statistics(listSize);
intList.randomize();
intList.computeMean();
intList.computeMedian();
intList.computeMode();
intList.displayStats();
System.out.println();
}
}
class Statistics
{
private int list[]; // the actual array of integers
private int size; // user-entered number of integers in the array
private double mean;
private double median;
private int mode;
public Statistics(int s)
{
size = s;
list = new int[size];
mean = median = mode = 0;
}
public void randomize()
{
Random rand = new Random(12345);
for (int k = 0; k < size; k++)
list[k] = rand.nextInt(31) + 1; // range of 1..31
}
public void computeMean()
{
double total=0;
for (int f = 0; f < size; f++)
{
total = total + list[f];
}
mean = total / size;
}
public void computeMedian()
{
int total2 = 0;
Arrays.sort(list);
if (size / 2 == 1)
{
// total2 =
}
else
{
total2 = size / 2;
median = list[total2];
}
}
public void computeMode()
{
// precondition: The list array has exactly 1 mode.
}
public void displayStats()
{
System.out.println(Arrays.toString(list));
System.out.println();
System.out.println("Mean: " + mean);
System.out.println("Median: " + median);
System.out.println("Mode: " + mode);
}
}

Here are two implementations for your median() and mode() methods:
public void computeMedian() {
Arrays.sort(list);
if ( (list.size & 1) == 0 ) {
// even: take the average of the two middle elements
median = (list[(size/2)-1] + list[(size/2)]) / 2;
} else {
// odd: take the middle element
median = list[size/2];
}
}
public void computeMode() {
// precondition: The list array has exactly 1 mode.
Map<Integer, Integer> values = new HashMap<Integer, Integer>();
for (int i=0; i < list.size; ++i) {
if (values.get(list[i]) == null) {
values.put(list[i], 1);
} else {
values.put(list[i], values.get(list[i])+1);
}
}
int greatestTotal = 0;
// iterate over the Map and find element with greatest occurrence
Iterator it = values.entrySet().iterator();
while (it.hasNext()) {
Map.Entry pair = (Map.Entry)it.next();
if (pair.getValue() > greatestTotal) {
mode = pair.getKey();
greatestTotal = pair.getValue();
}
it.remove();
}
}