yichuandemo.java

一个java实现的遗传算法程序

/*无约束条件 max f(x1,x2)=21.5+x1*sin(4*pi*x1)+x2sin(20*pi*x2)

-3.0<x1<12.1

4.1<x2<5.8

1%的变异

25%交叉

旋转转轮选择*/

/**
 * 实现Michalewicz
 * 
 * @author not attributable
 * @version 1.0
 */

public class yichuanDemo { 
	bestindival bd = null;
	String[] ipop = new String[10]; int gernation = 0; 

public yichuanDemo() 
{

  this.ipop = inialPops();
 } 
double calculatefitnessvalue(String str) 
{ // str为染色体，前面18个为x1表示部分，后面15个为x2表示部分
  String str1 = str.substring(0, 18);
  // System.out.println(str1);
  String str2 = str.substring(18);
  // System.out.println(str2);
  int b1 = Integer.parseInt(str1, 2);
  // System.out.println(b1);
  int b2 = Integer.parseInt(str2, 2);
  // System.out.println(b2);
  double x1 = -3.0 + b1 * (12.1 - (-3.0)) / (Math.pow(2, 18) - 1);
  // System.out.println(x1);
  double x2 = 4.1 + b2 * (5.8 - 4.1) / (Math.pow(2, 15) - 1);
  // System.out.println(x2);
  double fitness = 21.5 + x1 * Math.sin(4 * 3.1415926 * x1) + x2
    * Math.sin(20 * 3.1415926 * x2);
  //System.out.println("eval=f(" + x1 + "," + x2 + ")=" + fitness);
  return fitness;
 } 
String inialPop() 
{ // 初始化10个字符串
  String res = "";
  for (int i = 0; i < 33; i++) 
  {
   if (Math.random() > 0.5) 
   {
    res += "0";
   } else
   {
    res += "1";
   }  
   }
  return res;
 } 
String[] inialPops() 
{
  String[] ipop = new String[10];
  for (int i = 0; i < 10; i++) 
  {
   ipop[i] = inialPop();
  }
  return ipop; }
void select() 
{
  double evals[] = new double[10];// 所有染色体适应值
  double p[] = new double[10];// 各染色体选择概率
  double q[] = new double[10];// 累计概率
  double F = 0;
  for (int i = 0; i < 10; i++) 
  {
   evals[i] = calculatefitnessvalue(ipop[i]);
   if (bd == null) 
   {
    bd = new bestindival();
    bd.fitness = evals[i];
    bd.generations = 0;
    bd.str = ipop[i];
   } 
   else {
    if (evals[i] > bd.fitness)// 最好的记录下来
    {
     bd.fitness = evals[i];
     bd.generations = gernation;
     bd.str = ipop[i];
    }  
    }
   F = F + evals[i];// 所有染色体适应值总和 
   }
  for (int i = 0; i < 10; i++) 
  {
   p[i] = evals[i] / F;
   if (i == 0)
    q[i] = p[i];
   else
   {
    q[i] = q[i - 1] + p[i];
   }
  }
  for (int i = 0; i < 10; i++) 
  {   double r = Math.random();
   if (r <= q[0]) 
   {
    ipop[i] = ipop[0];   } 
   else {
    for (int j = 1; j < 10; j++)
    {
     if (r < q[j]) {
      ipop[i] = ipop[j];
      break;
     }
    }
   }
  } 
  } void cross() 
  { // 交叉率为25%，平均为25%的染色体进行交叉
  String temp1, temp2;
  for (int i = 0; i < 10; i++) 
  {
   if (Math.random() < 0.25)
   {
    double r = Math.random();
    int pos = (int) (Math.round(r * 1000)) % 33;
    if (pos == 0) 
    {
     pos = 1;
    }
    temp1 = ipop[i].substring(0, pos)
      + ipop[(i + 1) % 10].substring(pos);
    temp2 = ipop[(i + 1) % 10].substring(0, pos)
      + ipop[i].substring(pos);
    ipop[i] = temp1;
    ipop[(i + 1) / 10] = temp2;  
    } 
   }
 } 
  void mutation() {
  // 1%基因变异m*pop_size 共330个基因，为了使每个基因都相投机会发生变异，需要产生[1--330]上均匀分布的
  for (int i = 0; i < 4; i++) 
  {
   int num = (int) (Math.random() * 330 + 1);
   int chromosomeNum = (int) (num / 33) + 1; // 染色体号
   int mutationNum = num - (chromosomeNum - 1) * 33; // 基因号
   if (mutationNum == 0)
    mutationNum = 1;
   //System.out.println(num + "," + chromosomeNum + "," + mutationNum);
   chromosomeNum = chromosomeNum - 1;
   if(chromosomeNum>=10)
    chromosomeNum=9;
   //System.out.println("变异前" + ipop[chromosomeNum]);
   String temp;
   if (ipop[chromosomeNum].charAt(mutationNum - 1) == '0') 
   {
    if (mutationNum == 1) 
    {
     temp = "1" + ipop[chromosomeNum].substring(mutationNum);
    } else
    {
     if (mutationNum != 33) 
     {
      temp = ipop[chromosomeNum]
        .substring(0, mutationNum - 1)
        + "1"
        + ipop[chromosomeNum].substring(mutationNum);
     } else 
     {
      temp = ipop[chromosomeNum]
        .substring(0, mutationNum - 1)
        + "1";
     }
    }
   } 
   else 
   {
    if (mutationNum == 1) 
    {
     temp = "0" + ipop[chromosomeNum].substring(mutationNum);
    } else 
    {
     if (mutationNum != 33) 
     {
      temp = ipop[chromosomeNum]
        .substring(0, mutationNum - 1)
        + "0"
        + ipop[chromosomeNum].substring(mutationNum);
     } 
     else 
     {
      temp = ipop[chromosomeNum]
        .substring(0, mutationNum - 1)
        + "1";
     }
    }  
    }
   ipop[chromosomeNum] = temp;
   //System.out.println("变异后" + ipop[chromosomeNum]); 
   } 
   }
 
 void process()
 {
  for(int i=0;i<1000000;i++)
  {
   select();
   cross();
   mutation();
   gernation=i;
   
  }
  System.out.println("最优值"+bd.fitness+",代数"+bd.generations);
 } public static void main(String args[]) {
  yichuanDemo j = new yichuanDemo();
  // System.out.println(j.calculatefitnessvalue("000001010100101001101111011111110"));
  j.process(); }
}class bestindival { // 存储最佳的
 public int generations; public String str; public double fitness;}