Java算法---遗传算法示例

              用一个简单的实例带大家走进遗传算法的神奇世界。

  问题描述：

        求下面二元函数的最大值：

        


       遗传算法主要核心包括：编码，初始种群的产生，适应度计算，选择算子，交叉算子，变异算子。

      这个示例中采用二进制编码，x1,x2的范围在[1-7]直接，可以选择3位二进制进行编码，3为二进制编码刚好和每个数字对应。适应度计算，可以采取目标函数f(x1.x2)，算法具体实现如下：

 

import java.util.ArrayList;
import java.util.Collections;
import java.util.Random;
/**
* Created by yaobo on 2016/6/20.
*/
public class GAMAX {
public  static int MaxGen=1000;//最大遗传代数
public static int generate;//全局变量，控制循环的代数
double Pc=0.7;//交叉概率
double Pm=0.05;//变异概率
int Chromlenth=6;//染色体长度
int scale=10;// 种群规模
int everchromlength=3;//变量的编码长度
int bestIndex;//当前代最好个体的索引
int worstIndex;//当前代最坏个体的索引
Individual bestIndividual;//当前代最好的个体
Individual worstIndividual;//当前代最坏的个体
Individual currentBest=new Individual();
static double Rs=00.;//保存出现最好结果值
static int Rsindex=-1;//保存出现最好结果的代数
Individual[] population=new Individual[scale];//定义数组保存种群
Random random=new Random();//随机函数
public static void main(String[] args) {
GAMAX gamax=new GAMAX();
generate=0;//从0代开始
gamax.generateInitalPopulation();//产生第一代个体
gamax.evaluatePopulation();
while (generate<MaxGen){//循环迭代
generate++;
gamax.generateNextPopulation();
gamax.evaluatePopulation();
gamax.performEvolution();
gamax.printInf();
gamax.findBest();
}
System.out.println("最优结果"+Rs+"产生代数"+Rsindex);

}

void findBest(){//找到最好的结果和代数
if(Rs<bestIndividual.fitness){
Rs=bestIndividual.fitness;
Rsindex=bestIndex;
}
}

void generateNextPopulation(){//产生下一代样本
selectionOperator();
crossoverOperator();
mutationOperator();
}
void evaluatePopulation(){
calculateFitnessValue();
findBestAndWorstIndividual();

}
void generateInitalPopulation(){//产生第一代样本种群
StringBuffer sb;
Individual individual;
for(int i=0;i<scale;i++){
sb=new StringBuffer();
for(int j=0;j<Chromlenth;j++){
sb.append(((random.nextInt(65535)%10)<5)?'0':'1');
}
individual=new Individual();
individual.chrom=sb.toString();
population[i]=individual;
}
}

void mutationOperator(){//变异算子
double p;
for (int i=0;i<scale;i++){
for(int j=0;j<Chromlenth;j++) {
p = random.nextInt(65535) % 1000 / 1000.0;
if (p < Pm) {
char temp=population[i].chrom.charAt(j);
temp=(temp=='1'?'0':'1');
population[i].chrom=population[i].chrom.substring(0,j)+temp+population[i].chrom.substring(j+1);
}
}
}
}
void crossoverOperator(){//交叉算子
ArrayList<Integer> index=new ArrayList<Integer>();
int point;
double p;
String str1,str2;
for(int i=0;i<scale;i++){
index.add(i);
}
Collections.shuffle(index);//随机打乱样本，也就是随机配对
for(int i=0;i<scale-1;i=i+2){
p=random.nextInt(65535)%1000/1000.0;
if(p<Pc){//如果小于这个数，则进行交叉遗传
point=random.nextInt(65536)%(Chromlenth-1)+1;
str1=population[index.get(i)].chrom.substring(point);
str2=population[index.get(i+1)].chrom.substring(point);
population[index.get(i)].chrom= population[index.get(i)].chrom.substring(0,point)+str2;
population[index.get(i+1)].chrom= population[index.get(i+1)].chrom.substring(0,point)+str1;
}
}
}
void selectionOperator(){//选择算子并计算累积概率
double p;
double sum=0;
double[] cfitness=new double[scale];
int index;
Individual[] newpoputation=new Individual[scale];//新的种群
for (int i=0;i<scale;i++){
sum+=population[i].fitness;
}
for(int i=0;i<scale;i++){
cfitness[i]=population[i].fitness/sum;
}

for(int i=1;i<scale;i++){
cfitness[i]=cfitness[i]+cfitness[i-1];
}

for(int i=0;i<scale;i++){
p=random.nextInt(65535)%1000/1000.0;
index=0;
while (p>cfitness[index]){
index++;
}
newpoputation[i]=population[index];
}

for(int i=0;i<scale;i++){
population[i]=newpoputation[i];
}
}

void findBestAndWorstIndividual(){//找出当前最好的个体
bestIndividual=population[0];
worstIndividual=population[0];
for(int i=0;i<scale;i++){
if(population[i].fitness>bestIndividual.fitness){
bestIndividual=population[i];
bestIndex=i;
}
else if(population[i].fitness<worstIndividual.fitness){
worstIndividual=population[i];
worstIndex=i;
}
}
if(generate==0){
currentBest=bestIndividual;
}
else {
if(bestIndividual.fitness>currentBest.fitness){
currentBest=bestIndividual;
}
}

}
void calculateFitnessValue(){//计算个体的适应度值
String temp1,temp2;
double x1,x2;
for(int i=0;i<scale;i++){
temp1=Integer.valueOf(population[i].chrom.substring(0,everchromlength),2).toString();
temp2=Integer.valueOf(population[i].chrom.substring(everchromlength),2).toString();
x1=Integer.parseInt(temp1)*Integer.parseInt(temp1);
x2=Integer.parseInt(temp2)*Integer.parseInt(temp2);
population[i].fitness=x1+x2;

}

}
public void performEvolution(){
if(bestIndividual.fitness>currentBest.fitness){
currentBest=population[bestIndex];
}else {
population[worstIndex]=currentBest;
}
}
void printInf(){
double sum=0;
double average;
for (int i=0;i<scale;i++){
sum+=population[i].fitness;
}
average=sum/scale;
System.out.println("generate " + generate + " ava " + average + " best " + currentBest.fitness + " chrom " + currentBest.chrom);
}

}
class Individual{
String chrom;
double fitness;
}