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C++遺傳算法源程序

// CMVSOGA.h : main header file for the CMVSOGA.cpp
////////////////////////////////////////////////////////////////////
/////                                                          /////
/////                沈陽(yáng)航空工業(yè)學(xué)院動(dòng)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(píng)價(jià)個(gè)體，求最佳個(gè)體
void calculateobjectvalue();          //計(jì)算目標(biāo)函數(shù)值
void calculatefitnessvalue();          //計(jì)算適應(yīng)度函數(shù)值
void findbestandworstindividual();         //尋找最佳個(gè)體和最差個(gè)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(zhǎng)度應(yīng)該為變量的個(gè)數(shù)
  double value;
  double fitness;             //適應(yīng)度
};
double variabletop[variablenum];         //變量值
double variablebottom[variablenum];         //變量值
int popsize;              //種群大小
// int generation;              //世代數(shù)
int best_index;
int worst_index;
double crossoverrate;            //交叉率
double mutationrate;            //變異率
int maxgeneration;             //最大世代數(shù)
struct individual bestindividual;         //最佳個(gè)體
struct individual worstindividual;         //最差個(gè)體
struct individual current;              //當(dāng)前個(gè)體
struct individual current1;              //當(dāng)前個(gè)體
struct individual currentbest;          //當(dāng)前最佳個(gè)體
CList <struct individual,struct individual &> population;   //種群
CList <struct individual,struct individual &> newpopulation;  //新種群
CList <double,double> cfitness;          //存儲(chǔ)適應(yīng)度值
//怎樣使鏈表的數(shù)據(jù)是一個(gè)結(jié)構(gòu)體????主要是想把種群作成鏈表。節(jié)省空間。
};
#endif

執(zhí)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(yīng)該采用一定的策略來(lái)保證遺傳算法的初始化合理，采用產(chǎn)生正態(tài)分布隨機(jī)數(shù)初始化？選定中心點(diǎn)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(gè)真正的隨機(jī)數(shù)生成函數(shù)。
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(píng)價(jià)個(gè)體，求最佳個(gè)體
//{
// calculateobjectvalue();
// calculatefitnessvalue();   //在此步中因該按適應(yīng)度值進(jìn)行排序.鏈表的排序.
// findbestandworstindividual();
//}
void CMVSOGA:: calculateobjectvalue() //計(jì)算函數(shù)值，應(yīng)該由外部函數(shù)實(shí)現(xiàn)。主要因?yàn)槟繕?biāo)函數(shù)很復(fù)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(shù)進(jìn)行，在此只做結(jié)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(shù)進(jìn)行，在此只做結(jié)果的傳遞。
  population.SetAt(population.FindIndex(i),current);
}
}

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

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(shù)
  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(dāng)前個(gè)體中按概率選擇新種群,應(yīng)該加一個(gè)復(fù)制選擇,提高種群的平均適應(yīng)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(shí)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(yīng)度總值，以便歸一化,是已經(jīng)排序好的鏈。
{
  current=population.GetAt(population.FindIndex(i)); //取出來(lái)的值出現(xiàn)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(lái)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(jī)生成概率
  pindex=0;  //遍歷索引
  pc=cfitness.GetAt(cfitness.FindIndex(1));  //為什么取不到數(shù)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(shù)。即大于概率p的數(shù)應(yīng)該被選擇，選擇不滿則進(jìn)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(shù)線性交叉，浮點(diǎn)數(shù)適用,alpha ,beta是(0，1)之間的隨機(jī)數(shù)
          //對(duì)種群中兩兩交叉的個(gè)體選擇也是隨機(jī)選擇的。也可取beta=1-alpha;
          //current的變化會(huì)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(hào)
{
  index.InsertAfter (index.FindIndex(i),i);
}
for (i=0;i<popsize;i++)//打亂序號(hào)
{
  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(hào),按序號(hào)選擇兩個(gè)母體進(jìn)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(shí)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(yīng)度函數(shù)值計(jì)算，關(guān)鍵是適應(yīng)度函數(shù)的設(shè)計(jì)
          //current變化，這段程序變化較大，特別是排序。
{
int i;
double temp;//alpha,beta;//適應(yīng)度函數(shù)的尺度變化系數(shù)
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(píng)價(jià)結(jié)果,有冗余代碼,current變化，程序應(yīng)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?yàn)檫@一句，出現(xiàn)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(15053) 評(píng)論(7) 編輯收藏引用

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

我也寫過一點(diǎn)，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(zhì)
int sunlight[kMaxFlowers]; //陽(yáng)光
int nutrient[kMaxFlowers]; //養(yǎng)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(shù)
*/
int Fitness(int flower);

/**
花朵演化
*/
void Evolve();

/**
返回區(qū)間[start, end]的隨機(jī)數(shù)
*/
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(shù)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;
}
回復(fù) 更多評(píng)論

# re: C++遺傳算法源程序 2007-05-27 19:04 江水獸

啊哈當(dāng)初我也寫過只是沒有像這樣如此具有遺傳算法的特點(diǎn) 當(dāng)時(shí)只是為了選修課寫的一個(gè)具有基本功能的GA Program 回復(fù) 更多評(píng)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(fù) 更多評(píng)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(lián)系方式，有問題請(qǐng)教回復(fù) 更多評(píng)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(yè)論文，有問題想指教，有償求助，急，望回復(fù)，我的q是267497220 回復(fù) 更多評(píng)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(lái)共享！回復(fù) 更多評(píng)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(yè)論文，有問題想指教，我的QQ是709375489 回復(fù) 更多評(píng)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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