一個封裝遺傳算法基本操作的類

      最近一直在做項目的最後一部分，關於優化的問題，我用遺傳算法進行了優化計算，現在終於算是搞定了。下面首先簡單的介紹一下遺傳算法，然後給出我對遺傳算法基本操作封裝的一個類，最後給出如何使用這個類的一個工程例子。

      遺傳算法是一種現代優化算法，是根據生物進化思想而得出的一種全局優化算法。生物的進化是一個奇妙的優化過程，它通過基因遺傳，基因雜交，基因變異等規律產生適應環境變化的優良物種。它是一種模擬生物進化過程的一種全局尋優的方法。由於它具有很多的優點，如：隨機性較大，不需要精確的規則；容易形成通用的算法；收斂速度快等等，因此在現代優化計算中，使用遺傳算法的頻率是很高的。

    使用遺傳算法求解一個具體的問題時，一般要經過以下幾個步驟：編碼（常用的有0-1編碼）、種羣初始化、選擇操作、交叉操作、變異操作等。其中前兩個操作會隨着具體的問題不同而有所不同，而後面的三個操作基本上是相同的。而後面的三個操作是遺傳算法的核心操作，主要是爲了產生新一代種羣。下面就針對遺傳算法的基本操作作了一個簡單的封裝，具體的代碼如下：

.h頭文件：

//////////////////////////////////////////////////////////////////////////// // GeneAlgoOperator.h文件 #include <stdlib.h> #include <math.h> #include <afxwin.h> //一些默認值的定義 #define MAX_CHROMOSOME_LEN 100 //染色體最大長度爲100 #define MAX_POPULATION_SIZE 100 //種羣大小最大值 #define MAX_GENERATION 500 //最大的代數(迭代次數) #define CROSSRATE 0.2 //交叉率(0.2-0.99) #define VARIATIONRATE 0.002 //變異率(0.001-0.1) class CGeneAlgoOperator { public: CGeneAlgoOperator(int iChromosomeLen); CGeneAlgoOperator(); ~CGeneAlgoOperator(); struct sIndividual //個體結構 { char szChromosome[MAX_CHROMOSOME_LEN]; double dFitness; }; int m_iCurGeneration; //當前代數 int m_iMaxGeneration; //最大的代數(迭代次數)(便於修改) int m_iPopulationSize; //實際的種羣大小 double m_dCrossRate; //實際的交叉率 double m_dVariationRate; //實際的變異率 protected: int m_iChromosomeLen; //實際的染色體長度 public: int m_iCurGenBestIndex; //當前代中最佳個體索引值 int m_iCurGenWorstIndex; //當前代中最差個體索引值 struct sIndividual m_CurGenBestIndividual; //當前代中最好個體 struct sIndividual m_CurGenWorstIndividual; //當前代中最差個體 struct sIndividual m_BestIndividual; //目前最好個體 struct sIndividual m_Population[MAX_POPULATION_SIZE]; //種羣 //遺傳算法相關量設定 void SetGeneticValue(int iPopSize,int iMaxGeneration,double dCrossRate,double dVariationRate); //產生初始種羣 void GenerateInitialPopulation(); //查找當前代中最佳個體和最差個體,並更新目前最好個體 void FindBestAndWorstIndividual(); void GeneticSelectOperator(); //按比例選擇操作 void GeneticCrossOperator(); //交叉操作 void GeneticMutationOperator(); //變異操作 void GenerateNextPopulation(); //產生下一代種羣 //計算定長的二進制串的十進制值 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; //默認種羣大小爲50 m_iMaxGeneration = MAX_GENERATION; //最大的迭代次數默認爲500次 m_dCrossRate = CROSSRATE; //交叉率初始值爲0.2 m_dVariationRate = VARIATIONRATE; //變異率初始值爲0.002 m_iCurGeneration = 0; m_iChromosomeLen = iChromosomeLen; //染色體長度 } CGeneAlgoOperator::~CGeneAlgoOperator() { } //設定遺傳算法相關的變量值 void CGeneAlgoOperator::SetGeneticValue(int iPopSize,int iMaxGeneration,double dCrossRate,double dVariationRate) { m_iPopulationSize = iPopSize; //種羣大小 m_iMaxGeneration = iMaxGeneration; //最大的迭代次數 m_dCrossRate = dCrossRate; //交叉率 m_dVariationRate = dVariationRate; //變異率 m_iCurGeneration = 0; //當前代數設爲0 } //產生初始種羣 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'; } } //查找當前代中最佳個體和最差個體,並更新目前最好個體 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; //記錄最佳個體索引 } else if(m_Population[i].dFitness < m_CurGenWorstIndividual.dFitness) { m_CurGenWorstIndividual = m_Population[i]; m_iCurGenWorstIndex = i; //記錄最差個體索引 } } if(m_iCurGeneration == 0) { m_BestIndividual = m_CurGenBestIndividual; } else { if(m_CurGenBestIndividual.dFitness >= m_BestIndividual.dFitness) { m_BestIndividual = m_CurGenBestIndividual; //保存最佳個體 } } //將最好的個體直接替掉最差的個體進入下一代 m_Population[m_iCurGenWorstIndex] = m_BestIndividual; } //產生下一代種羣 void CGeneAlgoOperator::GenerateNextPopulation() { GeneticSelectOperator(); //選擇 GeneticCrossOperator(); //交叉 GeneticMutationOperator(); //變異 } //按比例進行的選擇操作 void CGeneAlgoOperator::GeneticSelectOperator() { int i,index; double dFitnessSum = 0.0; //總的適應度值 double dRand = 0.0; double dFitnessRate[MAX_POPULATION_SIZE]; //個體佔整個羣體的適應度的比率 struct sIndividual NewPopulation[MAX_POPULATION_SIZE]; //新的種羣 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(個體之間進行染色體隨機段進行交叉) 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++) //索引數組賦初值 { index[i] = i; } for(i=0;i<m_iPopulationSize;i++) //索引數組元素值進行隨機交換 { 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++) //最多進行m_iPopulationSize-1次交叉 { dRand = rand() % 1000 /1000.0; if(dRand < m_dCrossRate) { iPoint = rand() % (m_iChromosomeLen-1) + 1; //(範圍爲1到染色體長度減1) for(j=iPoint;j<m_iChromosomeLen;j++) //交換iPoint後所有的位 { 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; } } } } //變異操作 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) //變異 { m_Population[i].szChromosome[j] = (m_Population[i].szChromosome[j]=='0')?'1':'0'; } } } } //計算定長的二進制串的十進制值 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; }

    這個類封裝了遺傳算法的基本操作，使用這個類的對象可以進行種羣初始化（給出了染色體長度），選擇操作，交叉操作，變異操作以及查找種羣中最佳和最差個體等。由於，計算個體的適應度值會隨着具體問題的不同而有所不同，在本類中沒有給出計算適應度值的成員函數。下面是一個運用此操作類進行遺傳算法計算的工程例子，運行效果如下圖所示：(這個工程在我的資源中可以下載)

      

以下是這個工程的具體代碼：

主對話框代碼：

/////////////////////////////////////////////////////////////////////////// // GeneticAlgorithmDlg.h文件 #include <afxwin.h> #include "GeneAlgoOperator.h" #define Length1 10 //變量1佔染色體位數 #define Length2 10 //變量2佔染色體位數 //應用程序類 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; //遺傳算法操作對象 CString m_szParaFirst; //變量x1 CString m_szParaSecond; //變量x2 CString m_szResult; //函數值 int m_iChromosomeLen; //染色體長度 void CalculateFitness(); //計算適應度值 //染色體解碼 void DecodeChromosome(CString szChromosome,double* dPara1,double* dPara2); void StartGeneticCalculate(); //開始遺傳算法的求解 virtual BOOL OnInitDialog(); afx_msg OnCalculate(); //計算 afx_msg OnSetPara(); //重設參數按鈕響應 afx_msg OnReCalculate(); //重新計算按鈕響應 DECLARE_MESSAGE_MAP() }; //////////////////////////////////////////////////////////// // GeneticAlgorithmDlg.cpp文件 #include "GeneticAlgorithmDlg.h" #include "resource.h" #include "SetParaDlg.h" CGeneticAlgorithmApp theApp; //----------------------------------CGeneticAlgorithmApp類---------------------------------// BOOL CGeneticAlgorithmApp::InitInstance() { CGeneticAlgorithmDlg dlg; m_pMainWnd = &dlg; dlg.DoModal(); return FALSE; } //----------------------------------CGeneticAlgorithmDlg類---------------------------------// CGeneticAlgorithmDlg::CGeneticAlgorithmDlg(CWnd* pParentWnd):CDialog(IDD_GENETIC_DIALOG,pParentWnd) { m_iChromosomeLen = Length1 + Length2; //染色體長度賦初值 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; } //計算並顯示結果 CGeneticAlgorithmDlg::OnCalculate() { double dPara1 = 0.0; double dPara2 = 0.0; double dResult = 0.0; StartGeneticCalculate(); //開始遺傳算法的求解 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); //顯示數據 } //重設參數按鈕響應函數 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); } } //重新計算按鈕響應函數 CGeneticAlgorithmDlg::OnReCalculate() { double dPara1,dPara2,dResult; StartGeneticCalculate(); //開始遺傳算法的求解 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("已經重新進行了計算，更新結果顯示","信息",MB_OK|MB_ICONINFORMATION); UpdateData(FALSE); //顯示數據 } //開始遺傳算法的求解 void CGeneticAlgorithmDlg::StartGeneticCalculate() { int i = 0; m_pGeneAlgoOperator->GenerateInitialPopulation(); //產生初始種羣 m_pGeneAlgoOperator->m_iCurGeneration = 0; //當前代數置0 CalculateFitness(); //計算當前代所有個體的適應度值 m_pGeneAlgoOperator->FindBestAndWorstIndividual(); //查找最佳、最差個體 for(i=0;i<m_pGeneAlgoOperator->m_iMaxGeneration;i++) { m_pGeneAlgoOperator->GenerateNextPopulation(); //產生下一代種羣 CalculateFitness(); m_pGeneAlgoOperator->FindBestAndWorstIndividual(); i++; } } //計算種羣的適應度值 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; } } //解碼(-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); //對應的十進制值 iTemp2 = m_pGeneAlgoOperator->DecimalizeChromosome(szChromosome,Length1,Length2); //對應的十進制值 *dPara1 = 2*(iTemp1/1023.0) - 1; *dPara2 = 2*(iTemp2/1023.0) - 1; }

重設遺傳算法參數的對話框：

////////////////////////////////////////////////////////////// // 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(); }