问题
I have been implementing a simple genetic algorithm(GA) using Java. The steps of my GA are basically binary encoding, tournament selection, single-point crossover, and bit-wise mutation. Each individual of the population is represented by a class consisting of binary genes and a fitness value.
public class Individual {
int gene[];
int fitness;
public Individual(int n){
this.gene = new int[n];
}
}
The codes below does not include the bit-wise mutation part as I have been facing problem at the single-point crossover part of the GA. The way I have implemented the single-point crossover algorithm is by randomly finding a point for two consecutive Individual array elements and then swap their tails. The tail swapping is then repeated for each pair of Individual. I have also created the printGenome() method to print out all the arrays to compare, the resulting array after the crossover process is not properly swapped. I have tested my single-point crossover algorithm separately, it works. However when I tried to run it here in the codes below, the crossover simply does not work. May I know is it because there is something wrong within the Tournament Selection algorithm? Or is it something else(silly mistakes)? I have been reworking on it and still I could not pinpoint the error.
I would be grateful for any help and information provided! :)
public class GeneticAlgorithm {
public static void main(String[] args) {
int p = 10;
int n = 10;
Individual population[];
//create new population
population = new Individual[p];
for (int i = 0; i < p; i++) {
population[i] = new Individual(n);
}
//fills individual's gene with binary randomly
for (int i = 0; i < p; i++) {
for (int j = 0; j < n; j++) {
population[i].gene[j] = (Math.random() < 0.5) ? 0 : 1;
}
population[i].fitness = 0;
}
//evaluate each individual
for (int i = 0; i < p; i++) {
for (int j = 0; j < n; j++) {
if (population[i].gene[j] == 1) {
population[i].fitness++;
}
}
}
//total fitness check
System.out.println("Total fitness check #1 before tournament selection: " + getTotalFitness(population, p));
System.out.println("Mean fitness check #1 before tournament selection: " + getMeanFitness(population, p));
System.out.println("");
//tournament selection
Individual offspring[] = new Individual[p];
for (int i = 0; i < p; i++) {
offspring[i] = new Individual(n);
}
int parent1, parent2;
Random rand = new Random();
for (int i = 0; i < p; i++) {
parent1 = rand.nextInt(p); //randomly choose parent
parent2 = rand.nextInt(p); //randomly choose parent
if (population[parent1].fitness >= population[parent2].fitness) {
offspring[i] = population[parent1];
} else {
offspring[i] = population[parent2];
}
}
//total fitness check
System.out.println("Total fitness check #2 after tournament selection: " + getTotalFitness(offspring, p));
System.out.println("Mean fitness check #2 after tournament selection: " + getMeanFitness(offspring, p));
System.out.println("");
//genome check
System.out.println("Before Crossover: ");
printGenome(offspring, p, n);
//crossover
for (int i = 0; i < p; i = i + 2) {
int splitPoint = rand.nextInt(n);
for (int j = splitPoint; j < n; j++) {
int temp = offspring[i].gene[j];
offspring[i].gene[j] = offspring[i + 1].gene[j];
offspring[i + 1].gene[j] = temp;
}
}
//genome check
System.out.println("After Crossover:");
printGenome(offspring, p, n);
//evaluate each individual by counting the number of 1s after crossover
for (int i = 0; i < p; i++) {
offspring[i].fitness = 0;
for (int j = 0; j < n; j++) {
if (offspring[i].gene[j] == 1) {
offspring[i].fitness++;
}
}
}
//total fitness check
System.out.println("Total fitness check #3 after crossover: " + getTotalFitness(offspring, p));
System.out.println("Mean fitness check #3 after crossover: " + getMeanFitness(offspring, p));
}
public static void printGenome(Individual pop[], int p, int n) {
for (int i = 0; i < p; i++) {
for (int j = 0; j < n; j++) {
System.out.print(pop[i].gene[j]);
}
System.out.println("");
}
}
public static int getTotalFitness(Individual pop[], int p) {
int totalFitness = 0;
for (int i = 0; i < p; i++) {
totalFitness = totalFitness + pop[i].fitness;
}
return totalFitness;
}
public static double getMeanFitness(Individual pop[], int p) {
double meanFitness = getTotalFitness(pop, p) / (double) p;
return meanFitness;
}
}
回答1:
The problem is that, in your selection you are (most likely) duplicating individuals, when you say:
offspring[i] = population[parent1]
You are actually storing a reference to population[parent1] in offspring[i]. As a result your offspring array can contain the same reference multiple times, hence the same object will participate in crossover multiple times with multiple partners.
As a solution, you can store a clone instead of a reference to the same object. In Individual add:
public Individual clone(){
Individual clone = new Individual(gene.length);
clone.gene = gene.clone();
return clone;
}
And in your selection (note the added .clone()):
for (int i = 0; i < p; i++) {
parent1 = rand.nextInt(p); //randomly choose parent
parent2 = rand.nextInt(p); //randomly choose parent
if (population[parent1].fitness >= population[parent2].fitness) {
offspring[i] = population[parent1].clone();
} else {
offspring[i] = population[parent2].clone();
}
}
This way every element in offspring is a different object, even if the genome is the same.
That solves the Java part. Regarding the GA theory I hope some things, for instance your fitness measure are just placeholders, right?
来源：https://stackoverflow.com/questions/39906861/needs-help-for-genetic-algorithm-single-point-crossover-mechanism-in-java