C++實(shí)現(xiàn)遺傳算法
更新時(shí)間：2015年12月29日 16:09:30 作者：pass86
這篇文章主要介紹了C++實(shí)現(xiàn)遺傳算法，以實(shí)例形式較為詳細(xì)的分析了遺傳算法的C++實(shí)現(xiàn)技巧,具有一定參考借鑒價(jià)值,需要的朋友可以參考下
本文實(shí)例講述了C++實(shí)現(xiàn)簡單遺傳算法。分享給大家供大家參考。具體實(shí)現(xiàn)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 *); //種群初始化
void generatenextpopulation(); //生成下一代種群
void evaluatepopulation(); //評價(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(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();
}
# re: C++遺傳算法源程序
/********************************************************************
Filename: aiWorld.h
Purpose: 遺傳算法，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]; //溫度
int water[kMaxFlowers]; //水質(zhì)
int sunlight[kMaxFlowers]; //陽光
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;
}
希望本文所述對大家的C++程序設(shè)計(jì)有所幫助。
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