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C++遺傳算法源程序

// CMVSOGA.h : main header file for the CMVSOGA.cpp
////////////////////////////////////////////////////////////////////
/////                                                          /////
/////                沈陽航空工業學院動力工程系               /////
/////                       作者：李立新                       /////
/////                   完成日期：2006.08.02                   /////
/////                   修改日期：2007.04.10                                       /////
////////////////////////////////////////////////////////////////////

#if !defined(AFX_CMVSOGA_H__45BECA_61EB_4A0E_9746_9A94D1CCF767__INCLUDED_)
#define AFX_CMVSOGA_H__45BECA_61EB_4A0E_9746_9A94D1CCF767__INCLUDED_

#if _MSC_VER > 1000
#pragma once
#endif // _MSC_VER > 1000
#include "Afxtempl.h"
#define variablenum 14
class CMVSOGA
{
public:
CMVSOGA();
~CMVSOGA();
void selectionoperator();
void crossoveroperator();
void mutationoperator();
void initialpopulation(int, int ,double ,double,double *,double *);           //種群初始化
void generatenextpopulation();          //生成下一代種群
void evaluatepopulation();           //評價個體，求最佳個體
void calculateobjectvalue();          //計算目標函數值
void calculatefitnessvalue();          //計算適應度函數值
void findbestandworstindividual();         //尋找最佳個體和最差個體
void performevolution();
void GetResult(double *);
void GetPopData(CList <double,double>&);
void SetFitnessData(CList <double,double>&,CList <double,double>&,CList <double,double>&);
private:
struct individual
{
  double chromosome[variablenum];         //染色體編碼長度應該為變量的個數
  double value;
  double fitness;             //適應度
};
double variabletop[variablenum];         //變量值
double variablebottom[variablenum];         //變量值
int popsize;              //種群大小
// int generation;              //世代數
int best_index;
int worst_index;
double crossoverrate;            //交叉率
double mutationrate;            //變異率
int maxgeneration;             //最大世代數
struct individual bestindividual;         //最佳個體
struct individual worstindividual;         //最差個體
struct individual current;              //當前個體
struct individual current1;              //當前個體
struct individual currentbest;          //當前最佳個體
CList <struct individual,struct individual &> population;   //種群
CList <struct individual,struct individual &> newpopulation;  //新種群
CList <double,double> cfitness;          //存儲適應度值
//怎樣使鏈表的數據是一個結構體????主要是想把種群作成鏈表。節省空間。
};
#endif

執行文件：

// CMVSOGA.cpp : implementation file
//

#include "stdafx.h"
//#include "vld.h"
#include "CMVSOGA.h"
#include "math.h"
#include "stdlib.h"

#ifdef _DEBUG
#define new DEBUG_NEW
#undef THIS_FILE
static char THIS_FILE[] = __FILE__;
#endif
/////////////////////////////////////////////////////////////////////////////
// CMVSOGA.cpp
CMVSOGA::CMVSOGA()
{
best_index=0;
worst_index=0;
crossoverrate=0;            //交叉率
mutationrate=0;            //變異率
maxgeneration=0;
}
CMVSOGA::~CMVSOGA()
{
best_index=0;
worst_index=0;
crossoverrate=0;            //交叉率
mutationrate=0;            //變異率
maxgeneration=0;
population.RemoveAll();   //種群
newpopulation.RemoveAll();  //新種群
cfitness.RemoveAll();
}
void CMVSOGA::initialpopulation(int ps, int gen ,double cr ,double mr,double *xtop,double *xbottom)  //第一步，初始化。
{
//應該采用一定的策略來保證遺傳算法的初始化合理，采用產生正態分布隨機數初始化？選定中心點為多少？
int i,j;
popsize=ps;
maxgeneration=gen;
crossoverrate=cr;
mutationrate =mr;
for (i=0;i<variablenum;i++)
{
  variabletop[i] =xtop[i];
  variablebottom[i] =xbottom[i];
}
//srand( (unsigned)time( NULL ) );  //尋找一個真正的隨機數生成函數。
for(i=0;i<popsize;i++)
{
  for (j=0;j<variablenum ;j++)
  {
   current.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j];
  }
  current.fitness=0;
  current.value=0;
  population.InsertAfter(population.FindIndex(i),current);//除了初始化使用insertafter外,其他的用setat命令。
}
}
void CMVSOGA::generatenextpopulation()//第三步，生成下一代。
{
//srand( (unsigned)time( NULL ) );
selectionoperator();
crossoveroperator();
mutationoperator();
}
//void CMVSOGA::evaluatepopulation()   //第二步，評價個體，求最佳個體
//{
// calculateobjectvalue();
// calculatefitnessvalue();   //在此步中因該按適應度值進行排序.鏈表的排序.
// findbestandworstindividual();
//}
void CMVSOGA:: calculateobjectvalue() //計算函數值，應該由外部函數實現。主要因為目標函數很復雜。
{
int i,j;
    double x[variablenum];
for (i=0; i<popsize; i++)
{
  current=population.GetAt(population.FindIndex(i));
  current.value=0;
  //使用外部函數進行，在此只做結果的傳遞。
  for (j=0;j<variablenum;j++)
  {
   x[j]=current.chromosome[j];
   current.value=current.value+(j+1)*pow(x[j],4);
  }
  ////使用外部函數進行，在此只做結果的傳遞。
  population.SetAt(population.FindIndex(i),current);
}
}

void CMVSOGA::mutationoperator()  //對于浮點數編碼，變異算子的選擇具有決定意義。
          //需要guass正態分布函數，生成方差為sigma，均值為浮點數編碼值c。
{
// srand((unsigned int) time (NULL));
int i,j;
double r1,r2,p,sigma;//sigma高斯變異參數

for (i=0;i<popsize;i++)
{
  current=population.GetAt(population.FindIndex(i));

  //生成均值為current.chromosome，方差為sigma的高斯分布數
  for(j=0; j<variablenum; j++)
  {
   r1 = double(rand()%10001)/10000;
   r2 = double(rand()%10001)/10000;
   p = double(rand()%10000)/10000;
   if(p<mutationrate)
   {
    double sign;
    sign=rand()%2;
    sigma=0.01*(variabletop[j]-variablebottom [j]);
    //高斯變異
    if(sign)
    {
     current.chromosome[j] = (current.chromosome[j]
      + sigma*sqrt(-2*log(r1)/0.4323)*sin(2*3.1415926*r2));
    }
    else
    {
     current.chromosome[j] = (current.chromosome[j]
      - sigma*sqrt(-2*log(r1)/0.4323)*sin(2*3.1415926*r2));
    }
    if (current.chromosome[j]>variabletop[j])
    {
     current.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j];
    }
    if (current.chromosome[j]<variablebottom [j])
    {
     current.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j];
    }
   }
  }
  population.SetAt(population.FindIndex(i),current);
}
}
void CMVSOGA::selectionoperator()   //從當前個體中按概率選擇新種群,應該加一個復制選擇,提高種群的平均適應度
{
int i,j,pindex=0;
double p,pc,sum;
i=0;
j=0;
pindex=0;
p=0;
pc=0;
sum=0.001;
newpopulation.RemoveAll();
cfitness.RemoveAll();
  //鏈表排序
// population.SetAt (population.FindIndex(0),current); //多余代碼
for (i=1;i<popsize;i++)
{
  current=population.GetAt(population.FindIndex(i));
  for(j=0;j<i;j++)   //從小到大用before排列。
  {
   current1=population.GetAt(population.FindIndex(j));//臨時借用變量
   if(current.fitness<=current1.fitness)
   {
    population.InsertBefore(population.FindIndex(j),current);
    population.RemoveAt(population.FindIndex(i+1));
    break;
   }
  }
//  m=population.GetCount();
}
//鏈表排序
for(i=0;i<popsize;i++)//求適應度總值，以便歸一化,是已經排序好的鏈。
{
  current=population.GetAt(population.FindIndex(i)); //取出來的值出現問題.
  sum+=current.fitness;
}
for(i=0;i<popsize; i++)//歸一化
{
  current=population.GetAt(population.FindIndex(i)); //population 有值,為什么取出來的不正確呢??
  current.fitness=current.fitness/sum;
  cfitness.InsertAfter (cfitness .FindIndex(i),current.fitness);
}

for(i=1;i<popsize; i++)//概率值從小到大;
{
  current.fitness=cfitness.GetAt (cfitness.FindIndex(i-1))
   +cfitness.GetAt(cfitness.FindIndex(i));   //歸一化
  cfitness.SetAt (cfitness .FindIndex(i),current.fitness);
  population.SetAt(population.FindIndex(i),current);
}
for (i=0;i<popsize;)//輪盤賭概率選擇。本段還有問題。
{
  p=double(rand()%999)/1000+0.0001;  //隨機生成概率
  pindex=0;  //遍歷索引
  pc=cfitness.GetAt(cfitness.FindIndex(1));  //為什么取不到數值???20060910
  while(p>=pc&&pindex<popsize) //問題所在。
  {
   pc=cfitness.GetAt(cfitness .FindIndex(pindex));
   pindex++;
  }
  //必須是從index~popsize，選擇高概率的數。即大于概率p的數應該被選擇，選擇不滿則進行下次選擇。
  for (j=popsize-1;j<pindex&&i<popsize;j--)
  {
   newpopulation.InsertAfter (newpopulation.FindIndex(0),
    population.GetAt (population.FindIndex(j)));
   i++;
  }
}
for(i=0;i<popsize; i++)
{
  population.SetAt (population.FindIndex(i),
   newpopulation.GetAt (newpopulation.FindIndex(i)));
}
// j=newpopulation.GetCount();
// j=population.GetCount();
newpopulation.RemoveAll();
}

//current 變化后，以上沒有問題了。

void CMVSOGA:: crossoveroperator()   //非均勻算術線性交叉，浮點數適用,alpha ,beta是(0，1)之間的隨機數
          //對種群中兩兩交叉的個體選擇也是隨機選擇的。也可取beta=1-alpha;
          //current的變化會有一些改變。
{
int i,j;
double alpha,beta;
CList <int,int> index;
int point,temp;
double p;
// srand( (unsigned)time( NULL ) );
for (i=0;i<popsize;i++)//生成序號
{
  index.InsertAfter (index.FindIndex(i),i);
}
for (i=0;i<popsize;i++)//打亂序號
{
  point=rand()%(popsize-1);
  temp=index.GetAt(index.FindIndex(i));
  index.SetAt(index.FindIndex(i),
   index.GetAt(index.FindIndex(point)));
  index.SetAt(index.FindIndex(point),temp);
}
for (i=0;i<popsize-1;i+=2)
{//按順序序號,按序號選擇兩個母體進行交叉操作。
  p=double(rand()%10000)/10000.0;
  if (p<crossoverrate)
  {
   alpha=double(rand()%10000)/10000.0;
   beta=double(rand()%10000)/10000.0;
   current=population.GetAt(population.FindIndex(index.GetAt(index.FindIndex(i))));
   current1=population.GetAt(population.FindIndex(index.GetAt(index.FindIndex(i+1))));//臨時使用current1代替
   for(j=0;j<variablenum;j++)
   {
    //交叉
    double sign;
    sign=rand()%2;
    if(sign)
    {
     current.chromosome[j]=(1-alpha)*current.chromosome[j]+
      beta*current1.chromosome[j];
    }
    else
    {
     current.chromosome[j]=(1-alpha)*current.chromosome[j]-
      beta*current1.chromosome[j];
    }
    if (current.chromosome[j]>variabletop[j])  //判斷是否超界.
    {
     current.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j];
    }
    if (current.chromosome[j]<variablebottom [j])
    {
     current.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j];
    }
    if(sign)
    {
     current1.chromosome[j]=alpha*current.chromosome[j]+
      (1- beta)*current1.chromosome[j];
    }
    else
    {
     current1.chromosome[j]=alpha*current.chromosome[j]-
      (1- beta)*current1.chromosome[j];
    }
    if (current1.chromosome[j]>variabletop[j])
    {
     current1.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j];
    }
    if (current1.chromosome[j]<variablebottom [j])
    {
     current1.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j];
    }
   }
   //回代
  }
  newpopulation.InsertAfter (newpopulation.FindIndex(i),current);
  newpopulation.InsertAfter (newpopulation.FindIndex(i),current1);
}
ASSERT(newpopulation.GetCount()==popsize);
for (i=0;i<popsize;i++)
{
  population.SetAt (population.FindIndex(i),
   newpopulation.GetAt (newpopulation.FindIndex(i)));
}
newpopulation.RemoveAll();
index.RemoveAll();
}
void CMVSOGA:: findbestandworstindividual( )
{
int i;
bestindividual=population.GetAt(population.FindIndex(best_index));
worstindividual=population.GetAt(population.FindIndex(worst_index));
for (i=1;i<popsize; i++)
{
  current=population.GetAt(population.FindIndex(i));
  if (current.fitness>bestindividual.fitness)
  {
   bestindividual=current;
   best_index=i;
  }
  else if (current.fitness<worstindividual.fitness)
  {
   worstindividual=current;
   worst_index=i;
  }
}
population.SetAt(population.FindIndex(worst_index),
  population.GetAt(population.FindIndex(best_index)));
//用最好的替代最差的。
if (maxgeneration==0)
{
  currentbest=bestindividual;
}
else
{
  if(bestindividual.fitness>=currentbest.fitness)
  {
   currentbest=bestindividual;
  }
}
}
void CMVSOGA:: calculatefitnessvalue() //適應度函數值計算，關鍵是適應度函數的設計
          //current變化，這段程序變化較大，特別是排序。
{
int i;
double temp;//alpha,beta;//適應度函數的尺度變化系數
double cmax=100;
for(i=0;i<popsize;i++)
{
  current=population.GetAt(population.FindIndex(i));
  if(current.value<cmax)
  {
   temp=cmax-current.value;
  }
  else
  {
   temp=0.0;
  }
  /*
  if((population[i].value+cmin)>0.0)
{temp=cmin+population[i].value;}
else
{temp=0.0;
   }
  */
  current.fitness=temp;
  population.SetAt(population.FindIndex(i),current);
}
}
void CMVSOGA:: performevolution() //演示評價結果,有冗余代碼,current變化，程序應該改變較大
{
if (bestindividual.fitness>currentbest.fitness)
{
  currentbest=population.GetAt(population.FindIndex(best_index));
}
else
{
  population.SetAt(population.FindIndex(worst_index),currentbest);
}
}
void CMVSOGA::GetResult(double *Result)
{
int i;
for (i=0;i<variablenum;i++)
{
  Result[i]=currentbest.chromosome[i];
}
Result[i]=currentbest.value;
}

void CMVSOGA::GetPopData(CList <double,double>&PopData)
{
PopData.RemoveAll();
int i,j;
for (i=0;i<popsize;i++)
{
  current=population.GetAt(population.FindIndex(i));
  for (j=0;j<variablenum;j++)
  {
   PopData.AddTail(current.chromosome[j]);
  }
}
}
void CMVSOGA::SetFitnessData(CList <double,double>&PopData,CList <double,double>&FitnessData,CList <double,double>&ValueData)
{
int i,j;
for (i=0;i<popsize;i++)
{
  current=population.GetAt(population.FindIndex(i)); //就因為這一句，出現了很大的問題。
  for (j=0;j<variablenum;j++)
  {
   current.chromosome[j]=PopData.GetAt(PopData.FindIndex(i*variablenum+j));
  }
  current.fitness=FitnessData.GetAt(FitnessData.FindIndex(i));
  current.value=ValueData.GetAt(ValueData.FindIndex(i));
  population.SetAt(population.FindIndex(i),current);
}
FitnessData.RemoveAll();
PopData.RemoveAll();
ValueData.RemoveAll();
}

posted on 2007-05-26 08:14 唯月釋懷閱讀(15092) 評論(7) 編輯收藏引用

Feedback

# re: C++遺傳算法源程序 2007-05-26 17:38 pass86

我也寫過一點，從書上改編的。
/********************************************************************
Filename: aiWorld.h
Purpose: 遺傳算法，花朵演化。
Author: pass86
E-mail: pass86@gmail.com
Created: 2007/03/29
Id:
Copyright:
Licence:
*********************************************************************/
#ifndef AIWORLD_H_
#define AIWORLD_H_

#include <iostream>
#include <ctime>
#include <cstdlib>
#include <cmath>

#define kMaxFlowers 10

using std::cout;
using std::endl;

class ai_World
{
public:
ai_World()
{
srand(time(0));
}
~ai_World() {}

int temperature[kMaxFlowers]; //溫度
int water[kMaxFlowers]; //水質
int sunlight[kMaxFlowers]; //陽光
int nutrient[kMaxFlowers]; //養分
int beneficialInsect[kMaxFlowers]; //益蟲
int harmfulInsect[kMaxFlowers]; //害蟲

int currentTemperature;
int currentWater;
int currentSunlight;
int currentNutrient;
int currentBeneficialInsect;
int currentHarmfulInsect;

/**
第一代花朵
*/
void Encode();

/**
花朵適合函數
*/
int Fitness(int flower);

/**
花朵演化
*/
void Evolve();

/**
返回區間[start, end]的隨機數
*/
inline int tb_Rnd(int start, int end)
{
if (start > end)
return 0;
else
{
//srand(time(0));
return (rand() % (end + 1) + start);
}
}

/**
顯示數值
*/
void show();
};
// ----------------------------------------------------------------- //
void ai_World::Encode()
// ----------------------------------------------------------------- //

{
int i;

for (i=0;i<kMaxFlowers;i++)
{
temperature[i]=tb_Rnd(1,75);
water[i]=tb_Rnd(1,75);
sunlight[i]=tb_Rnd(1,75);
nutrient[i]=tb_Rnd(1,75);
beneficialInsect[i]=tb_Rnd(1,75);
harmfulInsect[i]=tb_Rnd(1,75);
}

currentTemperature=tb_Rnd(1,75);
currentWater=tb_Rnd(1,75);
currentSunlight=tb_Rnd(1,75);
currentNutrient=tb_Rnd(1,75);
currentBeneficialInsect=tb_Rnd(1,75);
currentHarmfulInsect=tb_Rnd(1,75);

currentTemperature=tb_Rnd(1,75);
currentWater=tb_Rnd(1,75);
currentSunlight=tb_Rnd(1,75);
currentNutrient=tb_Rnd(1,75);
currentBeneficialInsect=tb_Rnd(1,75);
currentHarmfulInsect=tb_Rnd(1,75);

}
// ----------------------------------------------------------------- //
int ai_World::Fitness(int flower)
// ----------------------------------------------------------------- //

{
int theFitness;

theFitness=abs(temperature[flower]-currentTemperature);
theFitness=theFitness+abs(water[flower]-currentWater);
theFitness=theFitness+abs(sunlight[flower]-currentSunlight);
theFitness=theFitness+abs(nutrient[flower]-currentNutrient);
theFitness=theFitness+abs(beneficialInsect[flower]-currentBeneficialInsect);
theFitness=theFitness+abs(harmfulInsect[flower]-currentHarmfulInsect);

return (theFitness);

}
// ----------------------------------------------------------------- //
void ai_World::Evolve()
// ----------------------------------------------------------------- //

{
int fitTemperature[kMaxFlowers];
int fitWater[kMaxFlowers];
int fitSunlight[kMaxFlowers];
int fitNutrient[kMaxFlowers];
int fitBeneficialInsect[kMaxFlowers];
int fitHarmfulInsect[kMaxFlowers];
int fitness[kMaxFlowers];
int i;
int leastFit=0;
int leastFitIndex;

for (i=0;i<kMaxFlowers;i++)
if (Fitness(i)>leastFit)
{
leastFit=Fitness(i);
leastFitIndex=i;
}

temperature[leastFitIndex]=temperature[tb_Rnd(0,kMaxFlowers - 1)];
water[leastFitIndex]=water[tb_Rnd(0,kMaxFlowers - 1)];
sunlight[leastFitIndex]=sunlight[tb_Rnd(0,kMaxFlowers - 1)];
nutrient[leastFitIndex]=nutrient[tb_Rnd(0,kMaxFlowers - 1)];
beneficialInsect[leastFitIndex]=beneficialInsect[tb_Rnd(0,kMaxFlowers - 1)];
harmfulInsect[leastFitIndex]=harmfulInsect[tb_Rnd(0,kMaxFlowers - 1)];

for (i=0;i<kMaxFlowers;i++)
{
fitTemperature[i]=temperature[tb_Rnd(0,kMaxFlowers - 1)];
fitWater[i]=water[tb_Rnd(0,kMaxFlowers - 1)];
fitSunlight[i]=sunlight[tb_Rnd(0,kMaxFlowers - 1)];
fitNutrient[i]=nutrient[tb_Rnd(0,kMaxFlowers - 1)];
fitBeneficialInsect[i]=beneficialInsect[tb_Rnd(0,kMaxFlowers - 1)];
fitHarmfulInsect[i]=harmfulInsect[tb_Rnd(0,kMaxFlowers - 1)];
}

for (i=0;i<kMaxFlowers;i++)
{
temperature[i]=fitTemperature[i];
water[i]=fitWater[i];
sunlight[i]=fitSunlight[i];
nutrient[i]=fitNutrient[i];
beneficialInsect[i]=fitBeneficialInsect[i];
harmfulInsect[i]=fitHarmfulInsect[i];
}

for (i=0;i<kMaxFlowers;i++)
{
if (tb_Rnd(1,100)==1)
temperature[i]=tb_Rnd(1,75);
if (tb_Rnd(1,100)==1)
water[i]=tb_Rnd(1,75);
if (tb_Rnd(1,100)==1)
sunlight[i]=tb_Rnd(1,75);
if (tb_Rnd(1,100)==1)
nutrient[i]=tb_Rnd(1,75);
if (tb_Rnd(1,100)==1)
beneficialInsect[i]=tb_Rnd(1,75);
if (tb_Rnd(1,100)==1)
harmfulInsect[i]=tb_Rnd(1,75);
}

}
void ai_World::show()
{
// cout << "\t temperature water sunlight nutrient beneficialInsect harmfulInsect\n";
cout << "current\t " << currentTemperature << "\t " << currentWater << "\t ";
cout << currentSunlight << "\t " << currentNutrient << "\t ";
cout << currentBeneficialInsect << "\t " << currentHarmfulInsect << "\n";
for (int i=0;i<kMaxFlowers;i++)
{
cout << "Flower " << i << ": ";
cout << temperature[i] << "\t ";
cout << water[i] << "\t ";
cout << sunlight[i] << "\t ";
cout << nutrient[i] << "\t ";
cout << beneficialInsect[i] << "\t ";
cout << harmfulInsect[i] << "\t ";
cout << endl;
}
}
#endif // AIWORLD_H_

//test.cpp
#include <iostream>
#include "ai_World.h"

using namespace std;

int main()
{
ai_World a;
a.Encode();
// a.show();
for (int i = 0; i < 10; i++)
{
cout << "Generation " << i << endl;
a.Evolve();
a.show();
}

system("PAUSE");
return 0;
}
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# re: C++遺傳算法源程序 2007-05-27 19:04 江水獸

啊哈當初我也寫過只是沒有像這樣如此具有遺傳算法的特點當時只是為了選修課寫的一個具有基本功能的GA Program 回復更多評論

# re: C++遺傳算法源程序 2007-06-25 17:33 7777

垃圾什么你都往上傳回復更多評論

# re: C++遺傳算法源程序 2007-10-21 08:15 starlet

作者不知道有沒有聯系方式，有問題請教回復更多評論

# re: C++遺傳算法源程序 2008-04-20 02:33 阿毛

你好，我在做遺傳算法解決vrp問題的畢業論文，有問題想指教，有償求助，急，望回復，我的q是267497220 回復更多評論

# re: C++遺傳算法源程序 2008-10-19 21:57 禇

謝謝，我如果有新的代碼，也給大家傳上來共享！回復更多評論

# re: C++遺傳算法源程序 2011-04-20 16:28 sjiao888

你好，我在做遺傳算法的畢業論文，有問題想指教，我的QQ是709375489 回復更多評論

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# re: C++遺傳算法源程序 2007-05-26 17:38 pass86

# re: C++遺傳算法源程序 2007-05-27 19:04 江水獸

# re: C++遺傳算法源程序 2007-06-25 17:33 7777

# re: C++遺傳算法源程序 2007-10-21 08:15 starlet

# re: C++遺傳算法源程序 2008-04-20 02:33 阿毛

# re: C++遺傳算法源程序 2008-10-19 21:57 禇

# re: C++遺傳算法源程序 2011-04-20 16:28 sjiao888

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