我使用了现有的遗传算法 here 并且重做了我但不知道我做错了什么 这是我得到的错误
线程“main”中的异常java.lang.NullPointerException at simpleGa.Algorithm.crossover（Algorithm.java:69）at simpleGa.Algorithm.evolvePopulation（Algorithm.java:34）at simpleGa.GAprisonerdilemma.main（GAprisonerdilemma.java:41）
我无法确切地知道错误的位置。阅读了很多关于NullPointerException的内容，但无法弄清楚
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());
}
}