C++遗传算法实现

概述C++遗传算法实现

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// CMVSOGA.h : main header file for the CMVSOGA.cpp////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////#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 14class CMVSOGA{public: CMVSOGA(); ~CMVSOGA(); voID selectionoperator(); voID crossoveroperator(); voID mutationoperator(); voID initialpopulation(int,int,double,double *,double *);           //种群初始化 voID generatenextpopulation();          //生成下一代种群 voID evaluatepopulation();           //评价个体，求最佳个体 voID calculateobjectvalue();          //计算目标函数值 voID calculatefitnessvalue();          //计算适应度函数值 voID findbestanDWorstindivIDual();         //寻找最佳个体和最差个体 voID performevolution();    voID GetResult(double *); voID GetPopData(CList <double,double>&); voID SetfitnessData(CList <double,double>&,CList <double,double>&);private: struct indivIDual {  double chromosome[variablenum];         //染色体编码长度应该为变量的个数  double value;           double fitness;             //适应度 }; double variabletop[variablenum];         //变量值 double variablebottom[variablenum];         //变量值 int popsize;              //种群大小// int generation;              //世代数 int best_index;   int worst_index; double crossoverrate;            //交叉率 double mutationrate;            //变异率 int maxgeneration;             //最大世代数 struct indivIDual bestindivIDual;         //最佳个体 struct indivIDual worstindivIDual;         //最差个体 struct indivIDual current;              //当前个体 struct indivIDual current1;              //当前个体 struct indivIDual currentbest;          //当前最佳个体 CList <struct indivIDual,struct indivIDual &> population;   //种群 CList <struct indivIDual,struct indivIDual &> newpopulation;  //新种群 CList <double,double> cfitness;          //存储适应度值 //怎样使链表的数据是一个结构体????主要是想把种群作成链表。节省空间。};#endif 执行文件：// 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_filestatic char THIS_file[] = __file__;#endif/////////////////////////////////////////////////////////////////////////////// CMVSOGA.cppCMVSOGA::CMVSOGA(){ best_index=0;   worst_index=0; crossoverrate=0;            //交叉率 mutationrate=0;            //变异率 maxgeneration=0;}CMVSOGA::~CMVSOGA(){ best_index=0;   worst_index=0; crossoverrate=0;            //交叉率 mutationrate=0;            //变异率 maxgeneration=0; population.RemoveAll();   //种群 newpopulation.RemoveAll();  //新种群 cfitness.RemoveAll(); }voID CMVSOGA::initialpopulation(int ps,int gen,double cr,double mr,double *xtop,double *xbottom)  //第一步，初始化。{ //应该采用一定的策略来保证遗传算法的初始化合理，采用产生正态分布随机数初始化？选定中心点为多少？ 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 ) );  //寻找一个真正的随机数生成函数。 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，评价个体，求最佳个体//{// calculateobjectvalue();// calculatefitnessvalue();   //在此步中因该按适应度值进行排序.链表的排序.// findbestanDWorstindivIDual();//}voID CMVSOGA:: calculateobjectvalue()  //计算函数值，应该由外部函数实现。主要因为目标函数很复杂。{ int i,j;    double x[variablenum]; for (i=0; i<popsize; i++) {  current=population.GetAt(population.Findindex(i));    current.value=0;  //使用外部函数进行，在此只做结果的传递。  for (j=0;j<variablenum;j++)  {   x[j]=current.chromosome[j];   current.value=current.value+(j+1)*pow(x[j],4);  }  ////使用外部函数进行，在此只做结果的传递。  population.SetAt(population.Findindex(i),current); }}voID CMVSOGA::mutationoperator()  //对于浮点数编码，变异算子的选择具有决定意义。          //需要guass正态分布函数，生成方差为sigma，均值为浮点数编码值c。{// srand((unsigned int) time (NulL)); int i,j; double r1,r2,p,sigma;//sigma高斯变异参数  for (i=0;i<popsize;i++) {  current=population.GetAt(population.Findindex(i));  //生成均值为current.chromosome，方差为sigma的高斯分布数  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]);    //高斯变异    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()   //从当前个体中按概率选择新种群,应该加一个复制选择,提高种群的平均适应度{ 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();  //链表排序// population.SetAt (population.Findindex(0),current); //多余代码 for (i=1;i<popsize;i++) {   current=population.GetAt(population.Findindex(i));  for(j=0;j<i;j++)   //从小到大用before排列。  {   current1=population.GetAt(population.Findindex(j));//临时借用变量   if(current.fitness<=current1.fitness)     {    population.InsertBefore(population.Findindex(j),current);    population.RemoveAt(population.Findindex(i+1));    break;   }  }//  m=population.GetCount(); } //链表排序 for(i=0;i<popsize;i++)//求适应度总值，以便归一化,是已经排序好的链。 {  current=population.GetAt(population.Findindex(i)); //取出来的值出现问题.  sum+=current.fitness; } for(i=0;i<popsize; i++)//归一化 {  current=population.GetAt(population.Findindex(i)); //population 有值,为什么取出来的不正确呢??  current.fitness=current.fitness/sum;  cfitness.InsertAfter (cfitness .Findindex(i),current.fitness); }  for(i=1;i<popsize; i++)//概率值从小到大; {  current.fitness=cfitness.GetAt (cfitness.Findindex(i-1))   +cfitness.GetAt(cfitness.Findindex(i));   //归一化  cfitness.SetAt (cfitness .Findindex(i),current.fitness);  population.SetAt(population.Findindex(i),current); } for (i=0;i<popsize;)//轮盘赌概率选择。本段还有问题。 {  p=double(rand()%999)/1000+0.0001;  //随机生成概率  pindex=0;  //遍历索引  pc=cfitness.GetAt(cfitness.Findindex(1));  //为什么取不到数值???20060910  while(p>=pc&&pindex<popsize) //问题所在。  {   pc=cfitness.GetAt(cfitness .Findindex(pindex));   pindex++;  }  //必须是从index~popsize，选择高概率的数。即大于概率p的数应该被选择，选择不满则进行下次选择。  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   变化后，以上没有问题了。voID CMVSOGA:: crossoveroperator()   //非均匀算术线性交叉，浮点数适用,Alpha,beta是(0，1)之间的随机数          //对种群中两两交叉的个体选择也是随机选择的。也可取beta=1-Alpha;          //current的变化会有一些改变。{ 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++)//生成序号 {  index.InsertAfter (index.Findindex(i),i); } for (i=0;i<popsize;i++)//打乱序号 {  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) {//按顺序序号,按序号选择两个母体进行交叉 *** 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))));//临时使用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])  //判断是否超界.    {     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() //适应度函数值计算，关键是适应度函数的设计          //current变化，这段程序变化较大，特别是排序。{ int  i; double temp;//Alpha,beta;//适应度函数的尺度变化系数 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() //演示评价结果,有冗余代码,current变化，程序应该改变较大{ 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,double>&fitnessData,double>&ValueData){ int i,j; for (i=0;i<popsize;i++) {    current=population.GetAt(population.Findindex(i)); //就因为这一句，出现了很大的问题。   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 唯月释怀 阅读(2874) 评论(6)  编辑 收藏 引用  Feedback# re: C++遗传算法源程序 2007-05-26 17:38 pass86 我也写过一点，从书上改编的。/********************************************************************filename: aiWorld.hPurpose: 遗传算法，花朵演化。Author: pass86E-mail: [email protected]Created: 2007/03/29ID:copyright:licence:*********************************************************************/#ifndef AIWORLD_H_#define AIWORLD_H_#include <iostream>#include <ctime>#include <cstdlib>#include <cmath>#define kMaxFlowers 10using std::cout;using std::endl;class ai_World{public:ai_World(){srand(time(0));}~ai_World() {}int temperature[kMaxFlowers]; //温度int water[kMaxFlowers]; //水质int sunlight[kMaxFlowers]; //阳光int nutrIEnt[kMaxFlowers]; //养分int beneficialinsect[kMaxFlowers]; //益虫int harmfulinsect[kMaxFlowers]; //害虫int currentTemperature;int currentWater;int currentSunlight;int currentNutrIEnt;int currentBeneficialinsect;int currentHarmfulinsect;/**第一代花朵*/voID Encode();/**花朵适合函数*/int fitness(int flower);/**花朵演化*/voID Evolve();/**返回区间[start,end]的随机数*/inline int tb_Rnd(int start,int end){if (start > end)return 0;else{//srand(time(0));return (rand() % (end + 1) + start);}}/**显示数值*/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);}// ----------------------------------------------------------------- //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(1,100)==1)sunlight[i]=tb_Rnd(1,100)==1)nutrIEnt[i]=tb_Rnd(1,100)==1)beneficialinsect[i]=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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