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 &);
 void SetFitnessData(CList &,CList &,CList &);
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  population;  //种群
 CList  newpopulation; //新种群
 CList  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;ivariabletop[j])
  {
   current.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j];
  }
  if (current.chromosome[j]=pc&&pindex index;
 int point,temp;
 double p;
// srand( (unsigned)time( NULL ) );
 for (i=0;ivariabletop[j]) //判断是否超界.
  {
   current.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j];
  }
  if (current.chromosome[j]variabletop[j])
  {
   current1.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j];
  }
  if (current1.chromosome[j]bestindividual.fitness)
 {
  bestindividual=current;
  best_index=i;
 }
 else if (current.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;icurrentbest.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&PopData) 
{
 PopData.RemoveAll();
 int i,j;
 for (i=0;i&PopData,CList &FitnessData,CList &ValueData)
{
 int i,j;
 for (i=0;i
#include 
#include 
#include 
 
#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;
 

void Encode();
 

int Fitness(int flower);
 

void Evolve();
 

inline int tb_Rnd(int start, int end)
{
if (start > end)
return 0;
else
{
//srand(time(0));
return (rand() % (end + 1) + start);
}
}
 

void show();
};
// ----------------------------------------------------------------- //
void ai_World::Encode()
// ----------------------------------------------------------------- //
 
{
int i;
 
for (i=0;ileastFit)
{
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
#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。