[原]遗传算法Java实现源代码

【Title】[原]遗传算法Java实现源代码
【Date】2013-04-07
【Abstract】以前学习遗传算法时，用Java实现的遗传算法程序，现整理分享出来。
【Keywords】wintys、遗传、算法、algorithm、种群、基因、个体、进化、染色体、适应度、Rosenbrock
【Environment】Windows 7、PowerDesigner16
【Author】wintys (wintys@gmail.com) http://wintys.cnblogs.com
【URL】http://www.cnblogs.com/wintys/archive/2013/04/07/genetic_algorithms.html

【Content】:

1、简介

此程序是对照《遗传算法原理及应用》（周明、孙树栋编著），附录所列C程序改编而来，用Java实现的遗传算法程序。相关理论请参考《遗传算法原理及应用》。

2、类图

类图由源代码经PowerDesigner反向生成。

（类图）

3、代码

3.1、染色体

   //染色体:Chromesone.java
   class Chromosome implements Cloneable{
       private StringBuffer chromosome;
       private int chromosomeLength;//染色体长度
       private char defaultChar; //默认基因填充字符

       public Chromosome(int chromosomeLength){
           chromosome = new StringBuffer(chromosomeLength);
           chromosome.setLength(chromosomeLength);
           defaultChar = '0';
           this.chromosomeLength = chromosomeLength;
       }

       //设置基因
       public boolean setGene(int begin , int end , String gene){
           int len = gene.length();

           if(len > end - begin + 1)
               return false;

           //index => chromosome , idx => gene
           for (int index = begin , idx = 0; index <= end; index++ , idx++) {
               if(idx < len)
                   chromosome.setCharAt(index , gene.charAt(idx));
               else
                   chromosome.setCharAt(index , defaultChar);
           }

           return true;
       }

       //获取基因
       public String getGene(int begin , int end){
           char[] dest = new char[end - begin + 1];
           chromosome.getChars(begin , end + 1 , dest , 0);

           return new String(dest);
       }

       public int getLength(){
           return chromosomeLength;
       }

       public String toString(){
           return chromosome.toString();
       }

       @Override
       public    Object clone()throws CloneNotSupportedException{
           Chromosome c = null;
           try{
               c = (Chromosome)super.clone();
               c.chromosome = new StringBuffer(chromosome);
           }catch(CloneNotSupportedException e ){
               System.out.println(e.getMessage());
           }

           return c;
       }
   }

3.2、个体

3.2.1、抽象个体

   //Individual.java
   abstract class Individual implements Cloneable{
       protected Chromosome chrom;//个体基因型:一个基因型染色体由多个基因组成
       protected int genelen;//基因长度
       protected double fitness;//适应度
       protected double targetValue;//目标函数值

       public abstract void coding();//编码
       public abstract void decode();//解码
       public abstract void calFitness();//计算个体适应度
       public abstract void generateIndividual();//随机产生个体
       public abstract void calTargetValue();//获取目标函数值

       public double getFitness(){
           return fitness;
       }

       public double getTargetValue(){
           return targetValue;
       }

       public int getChromlen(){
           return chrom.getLength();
       }

       public boolean setChrom(int begin , int end , String gene){
           return chrom.setGene(begin,end,gene);
       }

       public String getChrom(int begin , int end){
           return chrom.getGene(begin,end);
       }

       public void mutateSingleBit(int index){
           String gene , gn;
           gene = chrom.getGene(index , index);
           gn = gene.equals("0") ? "1":"0";
           chrom.setGene(index , index , gn);
       }

       @Override
       public Object clone(){
           Individual indv = null;

           try{
               indv = (Individual)super.clone();
               indv.chrom = (Chromosome)chrom.clone();
           }catch(CloneNotSupportedException e ){
               System.out.println(e.getMessage());
           }

           return indv;
       }
   }

3.2.2、Rosenbrock个体实现

   //RosenbrockIndividual.java
   class RosenbrockIndividual extends Individual {
       private double x1 , x2; // 个体表现型
       //基因型chromosome由 (x1 , x2)编码而成

       RosenbrockIndividual(int chromlen){
           genelen = 10;
           chrom = new Chromosome(chromlen);
       }

       //编码
       public void coding(){
           String code1,code2;
           code1 = codingVariable(x1);
           code2 = codingVariable(x2);

           chrom.setGene(0 , 9 , code1);
           chrom.setGene(10, 19 , code2);
       }

       //解码
       public void decode(){
           String gene1,gene2;

           gene1 = chrom.getGene(0 , 9);
           gene2 = chrom.getGene(10 , 19);

           x1 = decodeGene(gene1);
           x2 = decodeGene(gene2);
       }

       //计算目标函数值
       public void calTargetValue(){
           decode();
           targetValue = rosenbrock(x1 , x2);
       }

       //计算个体适应度
       public void calFitness(){
           fitness = getTargetValue();
       }

       private String codingVariable(double x){
           double y = (((x + 2.048) * 1023) / 4.096);
           String code = Integer.toBinaryString((int) y);

           StringBuffer codeBuf = new StringBuffer(code);
           for(int i = code.length(); i<genelen; i++)
               codeBuf.insert(0,'0');

           return codeBuf.toString();
       }

       private double decodeGene(String gene){
           int value ;
           double decode;
           value = Integer.parseInt(gene, 2);
           decode = value/1023.0*4.096 - 2.048;

           return decode;
       }

       public String toString(){
           String str = "";
           ///str = "基因型:" + chrom + " ";
           ///str+= "表现型:" + "[x1,x2]=" + "[" + x1 + "," + x2 + "]" + "\t";
           str+="函数值:" + rosenbrock(x1 , x2) + "\n";

           return    str;
       }

       /**
       *Rosenbrock函数:
       *f(x1,x2) = 100*(x1**2 - x2)**2 + (1 - x1)**2
       *在当x在[-2.048 , 2.048]内时，
       *函数有两个极大点:
       *f(2.048 , -2.048) = 3897.7342
       *f(-2.048,-2.048) = 3905.926227
       *其中后者为全局最大点。
       */
       public static double rosenbrock(double x1 , double x2){
           double fun;
           fun = 100*Math.pow((x1*x1 - x2) , 2) + Math.pow((1 - x1) , 2);

           return fun;
       }

       //随机产生个体
       public void generateIndividual(){
           x1 = Math.random() * 4.096 - 2.048;
           x2 = Math.random() * 4.096 - 2.048;

           //同步编码和适应度
           coding();
           calTargetValue();
           calFitness();
       }
   }

3.3、种群

   //Population.java
   class Population{
       private int generation; //种群的代数
       private int size;           //群体大小
       private Individual[] pop;   //种群
       private double averageFitness;    //平均适应度
       private double[] relativeFitness;   //相对适应度
       private int chromlen;//基因长度
       Individual bestIndividual;//当前代适应度最好的个体
       Individual worstIndividual;//当前代适应度最差的个体
       Individual currentBest;//到目前代为止最好的个体
       private int worstIndex;//bestIndividual对应的数组下标


       public Population(int size){
           this.generation = 0;
           this.size = size;

           this.pop = new Individual[size];
           this.averageFitness = 0;
           this.relativeFitness = new double[size];
           this.chromlen = 20;

           for(int i = 0; i < size; i++){
               pop[i] = new RosenbrockIndividual(chromlen);
           }
       }


       //初始化种群
       public void initPopulation(){
           for(int i = 0;i < size;i++){
               pop[i].generateIndividual();
           }

           findBestAndWorstIndividual();
       }

       //----------------------------------------------------
       //比例选择
       public void select(){
           double[] rouletteWheel; //赌盘
           Individual[] childPop = new Individual[size];

           calRelativeFitness();

           //产生赌盘
           rouletteWheel = new double[size];
           rouletteWheel[0] = relativeFitness[0];
           for(int i=1;i<size -1;i++){
               rouletteWheel[i] = relativeFitness[i] + rouletteWheel[i - 1];
           }
           rouletteWheel[size - 1] = 1;

           //进行赌盘选择,产生新种群
           for(int i = 0;i < size ; i++){
               double rnd = rand();
               for(int j = 0; j < size; j++){
                   if(rnd < rouletteWheel[j]){
                       childPop[i] = pop[j];
                       break;
                   }
               }
           }

           for(int i = 0;i < size; i++){
               pop[i] = childPop[i];
           }

           //return    childPop;
       }

       //求总适应度
       private double calTotalFitness(){
           double total = 0;
           for(int i = 0 ; i < size ;i++)
               total += pop[i].getFitness();
           return total;
       }

       //计算相对适应度
       public double[] calRelativeFitness(){
           double totalFitness = calTotalFitness();
           for(int i = 0 ;i < size ; i++){
               relativeFitness[i] = pop[i].getFitness() / totalFitness;
           }

           return relativeFitness;
       }

       //================================

       //------------------------------------------------------
       //单点交叉
       public void crossover(){
           for(int i = 0 ; i < size/2*2; i+=2){
               int rnd;
               //随机两两配对
               rnd = rand(i , size);

               if(rnd != i)
                   exchange(pop , i , rnd);

               rnd = rand(i , size);
               if(rnd != i+1)
                   exchange(pop , i + 1 , rnd);

               //交叉
               double random = rand();

               if(random < GeneticAlgorithms.crossoverRate){
                   cross(i);
               }
           }
       }

       //执行交叉操作
       private void cross(int i){
           String chromFragment1,chromFragment2;//基因片段

           int rnd = rand(0 , getChromlen() - 1);//交叉点为rnd之后,可能的位置有chromlen - 1个.
           chromFragment1 = pop[i].getChrom(rnd + 1 , getChromlen() - 1);
           chromFragment2 = pop[i+1].getChrom(rnd + 1 , getChromlen() - 1);

           pop[i].setChrom(rnd + 1 , getChromlen() - 1 , chromFragment2);
           pop[i+1].setChrom(rnd + 1 , getChromlen() - 1 , chromFragment1);
       }

       //产生随机数
       private int rand(int start , int end){//产生区间为[start , end)的随机整数
           return (int)(rand()*(end - start) + start);
       }

       //交换
       private void exchange(Individual[] p ,int src , int dest){
           Individual temp;
           temp = p[src];
           p[src] = p[dest];
           p[dest] = temp;
       }
       //==============================

       //-----------------------------------------------------
       //变异
       public void mutate(){
           for(int i = 0 ; i < size;i++){
               for(int j = 0 ;j < getChromlen(); j++){
                   if(rand() < GeneticAlgorithms.mutateRate){
                       pop[i].mutateSingleBit(j);
                       ///System.out.print("变异"+ i +" - "+ j + " ");///
                   }
               }
           }
       }
       //==============================

       //-----------------------------------------------------
       //进化
       public void evolve(){
           select();
           crossover();
           mutate();
           evaluate();
       }


       //==============================
       //计算目标函数值、适应度、找出最优个体。
       public void evaluate(){
           //同步目标函数值和适应度
           for(int i = 0; i < size; i++){
               pop[i].calTargetValue();
               pop[i].calFitness();
           }

           //使用最优保存策略(Elitist Model)保存最优个体
           findBestAndWorstIndividual();
           pop[worstIndex] = (Individual)currentBest.clone();

           generation++;
       }
       //找出适应度最大的个体
       public void findBestAndWorstIndividual(){
           bestIndividual = worstIndividual = pop[0];
           for(int i = 1; i <size;i++){
               if(pop[i].fitness > bestIndividual.fitness){
                   bestIndividual = pop[i];
               }
               if(pop[i].fitness < worstIndividual.fitness){
                   worstIndividual = pop[i];
                   worstIndex = i;
               }
           }

           if( generation == 0 ){//初始种群
               currentBest = (Individual)bestIndividual.clone();
           }else{
               if(bestIndividual.fitness > currentBest.fitness)
                   currentBest = (Individual)bestIndividual.clone();
           }
       }

       //判断进化是否完成
       public boolean isEvolutionDone(){
           if(generation < GeneticAlgorithms.maxGeneration)
               return false;
           return true;
       }

       //计算平均适应度
       public double calAverageFitness(){
           for(int i = 0 ; i < size; i++){
               averageFitness += pop[i].getFitness();
           }
           averageFitness/=size;

           return averageFitness;
       }

       //产生随机数
       private double rand(){
           return Math.random();
       }

       public int getChromlen(){
           return chromlen;
       }

       public void setGeneration(int generation){
           this.generation = generation;
       }

       public int getGeneration(){
           return generation;
       }
       /*
       public String printPop(Individual[] pop){
           String str="";
           for(int i = 0 ; i < size ; i++)
               str += pop[i];
           return str;
       }*/

       public String toString(){
           String str="";
           for(int i = 0 ; i < size ; i++)
               str += pop[i];
           return str;
       }
   }

3.4 测试

   //GeneticAlgorithms.java 给定参数，测试遗传算法
   import java.io.*;
   //2008-11-21
   class GeneticAlgorithms{
       public static double crossoverRate;//交叉概率
       public static double mutateRate;//变异概率
       public static int maxGeneration;//进化代数
       public static int populationSize;//群体大小

       static {
           //crossoverRate = 0.6;
           //mutateRate = 0.001;
           //maxGeneration = 100;
           //populationSize = 500;
           maxGeneration = 100;
           populationSize = 500;
           crossoverRate = 0.6;
           mutateRate = 0.001;
       }

       public static void main(String[] args)throws IOException{

           FileWriter fw = new FileWriter("result.txt");
           BufferedWriter bw = new BufferedWriter(fw);
           PrintWriter pw = new PrintWriter(bw);

           Population pop = new Population(populationSize);
           pop.initPopulation();

           pw.println("初始种群:\n" + pop);
           while(!pop.isEvolutionDone()){
               pop.evolve();
               pw.print("第" + pop.getGeneration() + "代Best:" + pop.bestIndividual );
               pw.print("第" + pop.getGeneration() + "代current:" + pop.currentBest );
               pw.println("");
           }
           pw.println();
           pw.println("第"+ pop.getGeneration() + "代群体:\n" + pop);

           pw.close();
       }

       public void print(){

       }
   }

【Reference】
[1]《遗传算法原理及应用》（周明、孙树栋编著）

【Attachment】

[1] 《遗传算法原理及应用》（周明、孙树栋编著）
[2]遗传算法Java源代码

posted @ 2013-04-07 13:25 wintys 阅读(8759) 评论(2) 编辑收藏举报

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