GA优化BP神经网络的权值程序

#include <stdlib.h> #include <time.h> #include "geneticnet.h" CGeneticNet::CGeneticNet() { srand((unsigned)(time(NULL))); for (int i=0;i<CGN_POPULATION;i++) { // Weights automatically initialized // in neural network constructor. m_pcPopulation[i] = new CSimpleNet; } } CGeneticNet::~CGeneticNet() { for (int i=0;i<CGN_POPULATION;i++) { delete m_pcPopulation[i]; } } CSimpleNet *CGeneticNet::Run() { float error = 0.0f; for (int iter=0;iter<CGN_MAXITER;iter++) { for (int i=0;i<CGN_POPULATION;i++) { error = ((m_pcPopulation[i]->Train(0,0,0) + m_pcPopulation[i]->Train(0,1,1) + m_pcPopulation[i]->Train(1,0,1) + m_pcPopulation[i]->Train(1,1,0)) / 4.0f); if (error < CGN_THRESHOLD) return m_pcPopulation[i]; m_fErrors[i] = error; } SortFitnesses(); NewPopulation(); } return NULL; } void CGeneticNet::SortFitnesses() { int n = CGN_POPULATION; int disorder = n; while (disorder) { disorder = 0; for(int i=1;i<n;i++) { if (m_fErrors[i] < m_fErrors[i-1]) { float m_fTemp = m_fErrors[i-1]; m_fErrors[i-1] = m_fErrors[i]; m_fErrors[i] = m_fTemp; CSimpleNet *tempnn = m_pcPopulation[i-1]; m_pcPopulation[i-1] = m_pcPopulation[i]; m_pcPopulation[i] = tempnn; disorder++; } } n--; } } void CGeneticNet::NewPopulation() { int id1, id2; float *wp1 = new float[9]; float *wp2 = new float[9]; for (int i=0;i<CGN_POPULATION / 2;i++) { id1 = rand() % CGN_POPULATION / 2; id2 = rand() % CGN_POPULATION / 2 + CGN_POPULATION / 2; m_pcPopulation[id1]->GetWeights(wp1); m_pcPopulation[id2]->GetWeights(wp2); for (int j=0;j<2;j++) { float temp; temp = wp1[j+6]; wp1[j+6] = wp2[j+6]; wp2[j+6] = temp; } if (rand() % 10 < 2) { for (int i=0;i<3;i++) { for (int j=0;j<3;j++) { m_pcPopulation[id1]->AlterWeights((float)(rand())/(32767/2) - 1); m_pcPopulation[id2]->AlterWeights((float)(rand())/(32767/2) - 1); } } } m_pcPopulation[id2]->SetWeights(wp1); } delete [] wp1; delete [] wp2; }