C++实现遗传算法

本文实例讲述了C++实现简单遗传算法。分享给大家供大家参考。具体实现方法如下：

// 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 14
class CMVSOGA
{
public:
 CMVSOGA();
 ~CMVSOGA();
 void selectionoperator();
 void crossoveroperator();
 void mutationoperator();
 void initialpopulation(int, int ,double ,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>&,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_FILE
static char THIS_FILE[] = __FILE__;
#endif
/////////////////////////////////////////////////////////////////////////////
// CMVSOGA.cpp
CMVSOGA::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))));//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,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();
}
 
# re: C++遗传算法源程序
/********************************************************************
Filename: aiWorld.h
Purpose: 遗传算法，花朵演化。
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]; //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);
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;
}

希望本文所述对大家的C++程序设计有所帮助。