一个封装遗传算法基本操作的类

      最近一直在做项目的最后一部分，关于优化的问题，我用遗传算法进行了优化计算，现在终于算是搞定了。下面首先简单的介绍一下遗传算法，然后给出我对遗传算法基本操作封装的一个类，最后给出如何使用这个类的一个工程例子。

      遗传算法是一种现代优化算法，是根据生物进化思想而得出的一种全局优化算法。生物的进化是一个奇妙的优化过程，它通过基因遗传，基因杂交，基因变异等规律产生适应环境变化的优良物种。它是一种模拟生物进化过程的一种全局寻优的方法。由于它具有很多的优点，如：随机性较大，不需要精确的规则；容易形成通用的算法；收敛速度快等等，因此在现代优化计算中，使用遗传算法的频率是很高的。

    使用遗传算法求解一个具体的问题时，一般要经过以下几个步骤：编码（常用的有0-1编码）、种群初始化、选择操作、交叉操作、变异操作等。其中前两个操作会随着具体的问题不同而有所不同，而后面的三个操作基本上是相同的。而后面的三个操作是遗传算法的核心操作，主要是为了产生新一代种群。下面就针对遗传算法的基本操作作了一个简单的封装，具体的代码如下：

.h头文件：

////////////////////////////////////////////////////////////////////////////// GeneAlgoOperator.h文件#include <stdlib.h>#include <math.h>#include <afxwin.h>//一些默认值的定义#define MAX_CHROMOSOME_LEN100//染色体最大长度为100#define MAX_POPULATION_SIZE 100//种群大小最大值#define MAX_GENERATION500//最大的代数(迭代次数)#define CROSSRATE0.2//交叉率(0.2-0.99)#define VARIATIONRATE0.002//变异率(0.001-0.1)class CGeneAlgoOperator{public:CGeneAlgoOperator(int iChromosomeLen);CGeneAlgoOperator();~CGeneAlgoOperator();struct sIndividual//个体结构{char szChromosome[MAX_CHROMOSOME_LEN];double dFitness;};int m_iCurGeneration;//当前代数int m_iMaxGeneration;//最大的代数(迭代次数)(便于修改)int m_iPopulationSize;//实际的种群大小double m_dCrossRate;//实际的交叉率double m_dVariationRate;//实际的变异率protected:int m_iChromosomeLen;//实际的染色体长度public:int m_iCurGenBestIndex;//当前代中最佳个体索引值int m_iCurGenWorstIndex;//当前代中最差个体索引值struct sIndividual m_CurGenBestIndividual;//当前代中最好个体struct sIndividual m_CurGenWorstIndividual;//当前代中最差个体struct sIndividual m_BestIndividual;//目前最好个体struct sIndividual m_Population[MAX_POPULATION_SIZE];//种群//遗传算法相关量设定void SetGeneticValue(int iPopSize,int iMaxGeneration,double dCrossRate,double dVariationRate);//产生初始种群void GenerateInitialPopulation();//查找当前代中最佳个体和最差个体,并更新目前最好个体void FindBestAndWorstIndividual();void GeneticSelectOperator();//按比例选择操作void GeneticCrossOperator();//交叉操作void GeneticMutationOperator();//变异操作void GenerateNextPopulation();//产生下一代种群//计算定长的二进制串的十进制值int DecimalizeChromosome(CString strChromosome,int iStart,int iLength);};

.cpp文件：

/////////////////////////////////////////////////////////////////////////////////// GeneAlgoOperator.cpp文件#include "GeneAlgoOperator.h"CGeneAlgoOperator::CGeneAlgoOperator(){}CGeneAlgoOperator::CGeneAlgoOperator(int iChromosomeLen){m_iPopulationSize = 50;//默认种群大小为50m_iMaxGeneration = MAX_GENERATION;//最大的迭代次数默认为500次m_dCrossRate = CROSSRATE;//交叉率初始值为0.2m_dVariationRate = VARIATIONRATE;//变异率初始值为0.002m_iCurGeneration = 0;m_iChromosomeLen = iChromosomeLen;//染色体长度}CGeneAlgoOperator::~CGeneAlgoOperator(){}//设定遗传算法相关的变量值void CGeneAlgoOperator::SetGeneticValue(int iPopSize,int iMaxGeneration,double dCrossRate,double dVariationRate){m_iPopulationSize = iPopSize;//种群大小m_iMaxGeneration = iMaxGeneration;//最大的迭代次数m_dCrossRate = dCrossRate;//交叉率m_dVariationRate = dVariationRate;//变异率m_iCurGeneration = 0;//当前代数设为0}//产生初始种群void CGeneAlgoOperator::GenerateInitialPopulation(){int i,j;for(i=0;i<m_iPopulationSize;i++){for(j=0;j<m_iChromosomeLen;j++){m_Population[i].szChromosome[j] = (rand()%10<5)?'0':'1';}m_Population[i].szChromosome[j] = '/0';}}//查找当前代中最佳个体和最差个体,并更新目前最好个体void CGeneAlgoOperator::FindBestAndWorstIndividual(){int i=0;m_CurGenBestIndividual = m_Population[0];m_CurGenWorstIndividual = m_Population[0];m_iCurGenBestIndex = 0;m_iCurGenWorstIndex = 0;for(i=0;i<m_iPopulationSize;i++){if(m_Population[i].dFitness > m_CurGenBestIndividual.dFitness){m_CurGenBestIndividual = m_Population[i];m_iCurGenBestIndex = i;//记录最佳个体索引}else if(m_Population[i].dFitness < m_CurGenWorstIndividual.dFitness){m_CurGenWorstIndividual = m_Population[i];m_iCurGenWorstIndex = i;//记录最差个体索引}}if(m_iCurGeneration == 0){m_BestIndividual = m_CurGenBestIndividual;}else{if(m_CurGenBestIndividual.dFitness >= m_BestIndividual.dFitness){m_BestIndividual = m_CurGenBestIndividual;//保存最佳个体}}//将最好的个体直接替掉最差的个体进入下一代m_Population[m_iCurGenWorstIndex] = m_BestIndividual;}//产生下一代种群void CGeneAlgoOperator::GenerateNextPopulation(){GeneticSelectOperator();//选择GeneticCrossOperator();//交叉GeneticMutationOperator();//变异}//按比例进行的选择操作void CGeneAlgoOperator::GeneticSelectOperator(){int i,index;double dFitnessSum = 0.0;//总的适应度值double dRand = 0.0;double dFitnessRate[MAX_POPULATION_SIZE];//个体占整个群体的适应度的比率struct sIndividual NewPopulation[MAX_POPULATION_SIZE];//新的种群for(i=0;i<m_iPopulationSize;i++){dFitnessSum = dFitnessSum + m_Population[i].dFitness;}for(i=0;i<m_iPopulationSize;i++){dFitnessRate[i] = m_Population[i].dFitness / dFitnessSum;}for(i=1;i<m_iPopulationSize;i++){dFitnessRate[i] = dFitnessRate[i-1] + dFitnessRate[i];}for(i=0;i<m_iPopulationSize;i++){dRand = rand() % 1000 /1000.0;index = 0;while(dRand > dFitnessRate[index]){index++;}NewPopulation[i] = m_Population[index];}for(i=0;i<m_iPopulationSize;i++){m_Population[i] = NewPopulation[i];}}//交叉操作(个体之间进行染色体随机段进行交叉)void CGeneAlgoOperator::GeneticCrossOperator(){int i,j;int index[MAX_POPULATION_SIZE];int iPoint,iTemp;double dRand;char ch;for(i=0;i<m_iPopulationSize;i++)//索引数组赋初值{index[i] = i;}for(i=0;i<m_iPopulationSize;i++)//索引数组元素值进行随机交换{iPoint = rand() % (m_iPopulationSize - i);iTemp = index[i];index[i] = index[iPoint+i];index[iPoint+i] = iTemp;}for(i=0;i<m_iPopulationSize-1;i++)//最多进行m_iPopulationSize-1次交叉{dRand = rand() % 1000 /1000.0;if(dRand < m_dCrossRate){iPoint = rand() % (m_iChromosomeLen-1) + 1;//(范围为1到染色体长度减1)for(j=iPoint;j<m_iChromosomeLen;j++)//交换iPoint后所有的位{ch = m_Population[index[i]].szChromosome[j];m_Population[index[i]].szChromosome[j] = m_Population[index[i+1]].szChromosome[j];m_Population[index[i+1]].szChromosome[j] = ch;}}}}//变异操作void CGeneAlgoOperator::GeneticMutationOperator(){int i,j;double dRand = 0.0;for(i=0;i<m_iPopulationSize;i++){for(j=0;j<m_iChromosomeLen;j++){dRand = rand()%1000/1000.0;if(dRand < m_dVariationRate)//变异{m_Population[i].szChromosome[j] = (m_Population[i].szChromosome[j]=='0')?'1':'0';}}}}//计算定长的二进制串的十进制值int CGeneAlgoOperator::DecimalizeChromosome(CString strChromosome,int iStart,int iLength){int i;int iRet = 0;CString strTemp = strChromosome.Mid(iStart,iLength);for(i=0;i<iLength;i++){if(strTemp.GetAt(i)!='0'){iRet = iRet + (int)pow(2,i);}}return iRet;}

    这个类封装了遗传算法的基本操作，使用这个类的对象可以进行种群初始化（给出了染色体长度），选择操作，交叉操作，变异操作以及查找种群中最佳和最差个体等。由于，计算个体的适应度值会随着具体问题的不同而有所不同，在本类中没有给出计算适应度值的成员函数。下面是一个运用此操作类进行遗传算法计算的工程例子，运行效果如下图所示：(这个工程在我的资源中可以下载)

      

以下是这个工程的具体代码：

主对话框代码：

///////////////////////////////////////////////////////////////////////////// GeneticAlgorithmDlg.h文件#include <afxwin.h>#include "GeneAlgoOperator.h"#define Length1 10//变量1占染色体位数#define Length2 10//变量2占染色体位数//应用程序类class CGeneticAlgorithmApp : public CWinApp{public:BOOL InitInstance();};class CGeneticAlgorithmDlg : public CDialog{public:CGeneticAlgorithmDlg(CWnd* pParentWnd=NULL);virtual ~CGeneticAlgorithmDlg();virtual void DoDataExchange(CDataExchange* pDX);CGeneAlgoOperator* m_pGeneAlgoOperator;//遗传算法操作对象CString m_szParaFirst;//变量x1CString m_szParaSecond;//变量x2CString m_szResult;//函数值int m_iChromosomeLen;//染色体长度void CalculateFitness();//计算适应度值//染色体解码void DecodeChromosome(CString szChromosome,double* dPara1,double* dPara2);void StartGeneticCalculate();//开始遗传算法的求解virtual BOOL OnInitDialog();afx_msg OnCalculate();//计算afx_msg OnSetPara();//重设参数按钮响应afx_msg OnReCalculate();//重新计算按钮响应DECLARE_MESSAGE_MAP()};////////////////////////////////////////////////////////////// GeneticAlgorithmDlg.cpp文件#include "GeneticAlgorithmDlg.h"#include "resource.h"#include "SetParaDlg.h"CGeneticAlgorithmApp theApp;//----------------------------------CGeneticAlgorithmApp类---------------------------------//BOOL CGeneticAlgorithmApp::InitInstance(){CGeneticAlgorithmDlg dlg;m_pMainWnd = &dlg;dlg.DoModal();return FALSE;}//----------------------------------CGeneticAlgorithmDlg类---------------------------------//CGeneticAlgorithmDlg::CGeneticAlgorithmDlg(CWnd* pParentWnd):CDialog(IDD_GENETIC_DIALOG,pParentWnd){m_iChromosomeLen = Length1 + Length2;//染色体长度赋初值m_pGeneAlgoOperator = new CGeneAlgoOperator(m_iChromosomeLen);}CGeneticAlgorithmDlg::~CGeneticAlgorithmDlg(){}BEGIN_MESSAGE_MAP(CGeneticAlgorithmDlg,CDialog)ON_BN_CLICKED(IDOK,OnCalculate)ON_BN_CLICKED(IDC_BTN_SETPARA,OnSetPara)ON_BN_CLICKED(IDC_BTN_RECALCULATE,OnReCalculate)END_MESSAGE_MAP()void CGeneticAlgorithmDlg::DoDataExchange(CDataExchange* pDX){CDialog::DoDataExchange(pDX);DDX_Text(pDX,IDC_EDIT_PARAFIRST,m_szParaFirst);DDX_Text(pDX,IDC_EDIT_PARASECOND,m_szParaSecond);DDX_Text(pDX,IDC_EDIT_RESULT,m_szResult);}BOOL CGeneticAlgorithmDlg::OnInitDialog(){CDialog::OnInitDialog();SetIcon(theApp.LoadIcon(IDI_ICON),FALSE);GetDlgItem(IDC_BTN_RECALCULATE)->EnableWindow(FALSE);return TRUE;}//计算并显示结果CGeneticAlgorithmDlg::OnCalculate(){double dPara1 = 0.0;double dPara2 = 0.0;double dResult = 0.0;StartGeneticCalculate();//开始遗传算法的求解DecodeChromosome(m_pGeneAlgoOperator->m_BestIndividual.szChromosome,&dPara1,&dPara2);dResult = m_pGeneAlgoOperator->m_BestIndividual.dFitness;m_szParaFirst.Format("%f",dPara1);m_szParaSecond.Format("%f",dPara2);m_szResult.Format("%f",dResult);UpdateData(FALSE);//显示数据}//重设参数按钮响应函数CGeneticAlgorithmDlg::OnSetPara(){CSetParaDlg dlg;dlg.m_iPopSize = m_pGeneAlgoOperator->m_iPopulationSize;dlg.m_iMaxGeneration = m_pGeneAlgoOperator->m_iMaxGeneration;dlg.m_dCrossRate = m_pGeneAlgoOperator->m_dCrossRate;dlg.m_dVariationRate = m_pGeneAlgoOperator->m_dVariationRate;int nResponse = dlg.DoModal();if(nResponse == IDOK){m_pGeneAlgoOperator->SetGeneticValue(dlg.m_iPopSize,dlg.m_iMaxGeneration,dlg.m_dCrossRate,dlg.m_dVariationRate);GetDlgItem(IDC_BTN_RECALCULATE)->EnableWindow(TRUE);}}//重新计算按钮响应函数CGeneticAlgorithmDlg::OnReCalculate(){double dPara1,dPara2,dResult;StartGeneticCalculate();//开始遗传算法的求解DecodeChromosome(m_pGeneAlgoOperator->m_BestIndividual.szChromosome,&dPara1,&dPara2);dResult = m_pGeneAlgoOperator->m_BestIndividual.dFitness;m_szParaFirst.Format("%f",dPara1);m_szParaSecond.Format("%f",dPara2);m_szResult.Format("%f",dResult);MessageBox("已经重新进行了计算，更新结果显示","信息",MB_OK|MB_ICONINFORMATION);UpdateData(FALSE);//显示数据}//开始遗传算法的求解void CGeneticAlgorithmDlg::StartGeneticCalculate(){int i = 0;m_pGeneAlgoOperator->GenerateInitialPopulation();//产生初始种群m_pGeneAlgoOperator->m_iCurGeneration = 0;//当前代数置0CalculateFitness();//计算当前代所有个体的适应度值m_pGeneAlgoOperator->FindBestAndWorstIndividual();//查找最佳、最差个体for(i=0;i<m_pGeneAlgoOperator->m_iMaxGeneration;i++){m_pGeneAlgoOperator->GenerateNextPopulation();//产生下一代种群CalculateFitness();m_pGeneAlgoOperator->FindBestAndWorstIndividual();i++;}}//计算种群的适应度值void CGeneticAlgorithmDlg::CalculateFitness(){int i;double dPara1,dPara2,dResult;for(i=0;i<m_pGeneAlgoOperator->m_iPopulationSize;i++){DecodeChromosome(m_pGeneAlgoOperator->m_Population[i].szChromosome,&dPara1,&dPara2);dResult = dPara1*dPara1-dPara1*dPara2+dPara2*dPara2;m_pGeneAlgoOperator->m_Population[i].dFitness = dResult;}}//解码(-1<=x1<=1,-1<=x2<=1)void CGeneticAlgorithmDlg::DecodeChromosome(CString szChromosome,double* dPara1,double* dPara2){int iTemp1,iTemp2;iTemp1 = m_pGeneAlgoOperator->DecimalizeChromosome(szChromosome,0,Length1);//对应的十进制值iTemp2 = m_pGeneAlgoOperator->DecimalizeChromosome(szChromosome,Length1,Length2);//对应的十进制值*dPara1 = 2*(iTemp1/1023.0) - 1;*dPara2 = 2*(iTemp2/1023.0) - 1;}

重设遗传算法参数的对话框：

//////////////////////////////////////////////////////////////// SetParaDlg.h文件#include "resource.h"#include <afxwin.h>class CSetParaDlg : public CDialog{public:CSetParaDlg(CWnd* pParentWnd=NULL);virtual ~CSetParaDlg();virtual void DoDataExchange(CDataExchange* pDX);int m_iPopSize;int m_iMaxGeneration;double m_dCrossRate;double m_dVariationRate;virtual BOOL OnInitDialog();afx_msg void OnOK();DECLARE_MESSAGE_MAP()};/////////////////////////////////////////////////////////// SetParaDlg.cpp文件#include "SetParaDlg.h"CSetParaDlg::CSetParaDlg(CWnd* pParentWnd):CDialog(IDD_SETPARA_DIALOG,pParentWnd){}CSetParaDlg::~CSetParaDlg(){}BEGIN_MESSAGE_MAP(CSetParaDlg,CDialog)ON_BN_CLICKED(IDOK,OnOK)END_MESSAGE_MAP()void CSetParaDlg::DoDataExchange(CDataExchange* pDX){CDialog::DoDataExchange(pDX);DDX_Text(pDX,IDC_EDIT_POPSIZE,m_iPopSize);DDV_MinMaxInt(pDX,m_iPopSize,50,100);DDX_Text(pDX,IDC_EDIT_MAXGENERATION,m_iMaxGeneration);DDV_MinMaxInt(pDX,m_iMaxGeneration,500,10000);DDX_Text(pDX,IDC_EDIT_CROSSRATE,m_dCrossRate);DDV_MinMaxDouble(pDX,m_dCrossRate,0.2,0.99);DDX_Text(pDX,IDC_EDIT_VARIATIONRATE,m_dVariationRate);DDV_MinMaxDouble(pDX,m_dVariationRate,0.001,0.1);}BOOL CSetParaDlg::OnInitDialog(){CDialog::OnInitDialog();SetIcon(AfxGetApp()->LoadIcon(IDI_ICON),TRUE);UpdateData(FALSE);return TRUE;}void CSetParaDlg::OnOK(){UpdateData(TRUE);CDialog::OnOK();}