import java.util.Collections;
import java.util.Vector;
public class Metaheuristic {
private static int[] DATA;
private static int NUM_CHROMOSOMES ;
private static int MAX_POWER;
private static int MAX_NUMBER;
private static int FITNESS_THRESHOLD;
private static float MUTATE = (float) .05;
private Vector population;
private boolean done = true;
int numRuns = 0;
public void GeneticAlgorithm (int[] data, int target, int n){
NUM_CHROMOSOMES = n;
MAX_POWER = data.length;
MAX_NUMBER = (int) Math.pow(2, MAX_POWER) - 1;
FITNESS_THRESHOLD = target;
DATA = new int[data.length];
DATA = data;
Metaheuristic s = new Metaheuristic();
s.start();
//System.out.println("s");
}
public Metaheuristic(){
generateRandomPopulation();
}
private void generateRandomPopulation(){
System.out.println("***Randomly Generating population with: " + NUM_CHROMOSOMES + " Chromosome(s)***");
population = new Vector();
for(int i = 0; i < NUM_CHROMOSOMES; ++i){
int randomValue = (int) (Math.random()*MAX_NUMBER);
population.add(new Chromosome(randomValue, MAX_POWER));
}
System.out.println("First Population: " + population +"\n");
}
public void start(){
Collections.sort(population);
Chromosome fitess = (Chromosome) population.lastElement();
done = fitess.fitness(DATA, FITNESS_THRESHOLD) >= MAX_POWER? true:false;
if(done){
System.out.println("DONE, solution found: " + fitess);
}
else{
numRuns++;
System.out.println("FITESS: " + fitess + " fitness: " + fitess.fitness(DATA, FITNESS_THRESHOLD ));
generateNewPopulation();
start();
}
}
private void generateNewPopulation(){
System.out.println("***Generating New Population");
Vector temp = new Vector();
for(int i = 0; i < population.size()/2; ++i){
Chromosome p1 = selectParent();
Chromosome p2 = selectParent();
temp.add(cross1(p1, p2));
temp.add(cross2(p1, p2));
}
population.clear();
population.addAll(temp);
System.out.println("New Population: " + population + "\n");
}
private Chromosome selectParent(){
int delta = population.size();
delta = NUM_CHROMOSOMES - NUM_CHROMOSOMES/2;
int num = (int) (Math.random()*10 + 1);
int index;
if(num >= 4){
index = (int) (Math.random()*delta + NUM_CHROMOSOMES/2);
}
else{
index = (int) (Math.random()*delta);
}
return (Chromosome) population.get(index);
}
private Chromosome cross1(Chromosome parent1, Chromosome parent2){
String bitS1 = parent1.getBitString();
String bitS2 = parent2.getBitString();
int length = bitS1.length();
String newBitString = bitS1.substring(0, length/2) + bitS2.substring(length/2, length);
Chromosome offspring = new Chromosome();
offspring.setBitString(newBitString);
if(shouldMutate()){
mutate(offspring);
}
return offspring;
}
private Chromosome cross2(Chromosome parent1, Chromosome parent2){
String bitS1 = parent1.getBitString();
String bitS2 = parent2.getBitString();
int length = bitS1.length();
String newBitString = bitS2.substring(0, length/2) + bitS1.substring(length/2, length);
Chromosome offspring = new Chromosome();
offspring.setBitString(newBitString);
if(shouldMutate()){
mutate(offspring);
}
return offspring;
}
private boolean shouldMutate(){
double num = Math.random();
int number = (int) (num*100);
num = (double) number/100;
return (num <= MUTATE);
}
private void mutate(Chromosome offspring){
String s = offspring.getBitString();
int num = s.length();
int index = (int) (Math.random()*num);
String newBit = flip(s.substring(index, index+1));
String newBitString = s.substring(0, index) + newBit + s.substring(index+1, s.length());
offspring.setBitString(newBitString);
}
private String flip(String s){
return s.equals("0")? "1":"0";
}
public static void main(String[] args) {
double average = 0;
int sum = 0;
for(int i = 0; i < 10; ++i){
Metaheuristic s = new Metaheuristic();
s.start();
sum = sum + s.numRuns;
average = (double) sum / (double)(i+1);
System.out.println("Number of runs: " + s.numRuns);
}
System.out.println("average runs: " + average);
}
}
import java.lang.Comparable;
public class Chromosome implements Comparable{
protected String bitString;
public static int[] DATA;
public int TARGET;
public Chromosome(){
}
public Chromosome(int value, int length){
bitString = convertIntToBitString(value, length);
}
public void setBitString(String s){
bitString = s;
}
public String getBitString(){
return bitString;
}
public int compareTo(Object o) {
Chromosome c = (Chromosome) o;
int num = countOnes(this.bitString) - countOnes(c.getBitString());
return num;
}
public int fitness(int[] data, int target){
DATA = new int[data.length];
System.arraycopy(data, 0, DATA, 0, data.length);
TARGET = target;
return countOnes(bitString);
}
public boolean equals(Object o){
if(o instanceof Chromosome){
Chromosome c = (Chromosome) o;
return c.getBitString().equals(bitString);
}
return false;
}
public int hashCode(){
return bitString.hashCode();
}
public String toString(){
return "Chromosome: " + bitString;
}
public static int countOnes(String bits){
int sum = 0;
for(int i = 0; i < bits.length(); ++ i){
String test = bits.substring(i, i+1);
sum = sum + (DATA[i]*Integer.parseInt(test));
}
return sum;
}
public static String convertIntToBitString(int val, int length){
int reval = val;
StringBuffer bitString = new StringBuffer(length);
for(int i = length-1; i >=0; --i ){
if( reval - (Math.pow(2, i)) >= 0 ){
bitString.append("1");
reval = (int) (reval - Math.pow(2, i));
}
else{
bitString.append("0");
}
}
return bitString.toString();
}
/* public static void main(String[] args){
//System.out.println(convertIntToBitString(2046, 10));
Chromosome c = new Chromosome(1234, 10);
System.out.println(c.fitness());
}*/
}
我的适应度函数是f(x ) = s · (C − P(x )) + (1 − s) · P(x )我C要达到的目标值和P(*x ) = (Sigma) wixi，wi元素的集合在哪里并且xi是 0 或 1（选择或不选择元素）。也是s0 或 1，取决于 p(x) 值。请帮我写这个健身。我刚刚尝试过，但程序运行时出错。