C++遺傳演算法類檔案執行個體分析_C 語言

本文所述為C++實現的遺傳演算法的類檔案執行個體。一般來說遺傳演算法可以解決許多問題，希望本文所述的C++遺傳演算法類檔案，可協助你解決更多問題，並且代碼中為了便於讀者更好的理解，而加入了豐富的注釋內容，是新手學習遺傳演算法不可多得的參考代碼。

具體代碼如下所示：

#include "stdafx.h"#include<iostream>#include<cstdio>#include<cstdlib>#include<cmath>#include<ctime>//把日期和時間轉換為字串using namespace std;//Parametes setting           #define POPSIZE 200   //population size #define MAXGENS 1000  //max number of generation #define NVARS 2     //no of problem variables #define PXOVER  0.75 //probalility of crossover #define PMUTATION 0.15 //probalility of mutation #define TRUE 1#define FALSE 0#define LBOUND 0    #define UBOUND 12   #define STOP 0.001int generation;     //current generation noint cur_best;      //best individualdouble diff;      FILE *galog;      //an output filestruct genotype{   double gene[NVARS];   //a string of variables基因變數   double upper[NVARS];  //individual's variables upper bound 基因變數取值上確界   double lower[NVARS];  //individual's batiables lower bound 基因變數取值下確界   double fitness;     //individual's fitness個體適應值   double rfitness;    //relative fitness個體適應值占種群適應值比例   double cfitness;    //curmulation fitness個體適應值的累加比例 };struct genotype population[POPSIZE+1]; //population 當前種群 population[POPSIZE]用於存放個體最優值並假設最優個體能存活下去//在某些遺傳演算法中最優值個體並不一定能夠存活下去struct genotype newpopulation[POPSIZE+1]; //new population replaces the old generation 子種群 /*Declaration of procedures used by the gentic algorithm*/ void initialize(void);          //初始化函數 double randval(double,double);      //隨機函數 double funtion(double x1,double x2);  //目標函數 void evaluate(void);          //評價函數 void keep_the_best(void);        //保留最優個體 void elitist(void);            //當前種群與子代種群最優值比較 void select(void); void crossover(void);          //基因重組函數 void swap(double *,double *);      //交換函數 void mutate(void);            //基因突變函數 double report(void);          //資料記錄函數void initialize(void) {  int i,j;   for(i=0;i<NVARS;i++)   {    for(j=0;j<POPSIZE+1;j++)    {       if(!i)       {        population[j].fitness=0;        population[j].rfitness=0;        population[j].cfitness=0;       }      population[j].lower[i]=LBOUND;      population[j].upper[i]=UBOUND;      population[j].gene[i]=randval(population[j].lower[i],population[j].upper[i]);     }   } }//***************************************************************************//Random value generator:generates a value within bounds//*************************************************************************** double randval(double low,double high) {   double val;   val=((double)(rand()%10000)/10000)*(high-low)+low;  return val; }//目標函數 double funtion(double x,double y){  double result1=sqrt(x*x+y*y)+sqrt((x-12)*(x-12)+y*y)+sqrt((x-8)*(x-8)+(y-6)*(y-6));  return result1;} //*************************************************************************** //Evaluation function:evaluate the individual's fitness.評價函數給出個體適應值//Each time the function is changes,the code has to be recompl //*************************************************************************** void evaluate(void) {  int mem;  int i;  double x[NVARS];  for(mem=0;mem<POPSIZE;mem++)   {    for(i=0;i<NVARS;i++)    x[i]=population[mem].gene[i];    population[mem].fitness=funtion(x[0],x[1]);//將目標函數值作為適應值  } } //*************************************************************************************** //Keep_the_best function:This function keeps track of the best member of the population.//找出種群中的個體最優值並將其移動到最後//*************************************************************************************** void keep_the_best() {   int mem;   int i;   cur_best=0;   for(mem=0;mem<POPSIZE;mem++)//找出最高適應值個體  {     if(population[mem].fitness<population[cur_best].fitness)     {       cur_best=mem;          }  }  //將最優個體複製至population[POSIZE]   if(population[cur_best].fitness<=population[POPSIZE].fitness||population[POPSIZE].fitness<1)//防止出現種群基因退化 故保留曆史最優個體  {    population[POPSIZE].fitness=population[cur_best].fitness;    for(i=0;i<NVARS;i++)    population[POPSIZE].gene[i]=population[cur_best].gene[i];  }  } //*************************************************************************** //last in the array.If the best individual from the new populatin is better//than the best individual from the previous population ,then copy the best //from the new population;else replace the worst individual from the current //population with the best one from the previous generation.防止種群最優值退化//*************************************************************************** void elitist(){   int i;  double best,worst;//適應值  int best_mem,worst_mem;//序號  best_mem=worst_mem=0;  best=population[best_mem].fitness;//最高適應值初始化  worst=population[worst_mem].fitness;//最低適應值初始化  for(i=1;i<POPSIZE;i++)//找出最高和最低適應值 演算法有待改進   {         if(population[i].fitness<best)     {       best=population[i].fitness;      best_mem=i;     }    if(population[i].fitness>worst)     {       worst=population[i].fitness;      worst_mem=i;    }     }  if(best<=population[POPSIZE].fitness)//賦值   {    for(i=0;i<NVARS;i++)       population[POPSIZE].gene[i]=population[best_mem].gene[i];    population[POPSIZE].fitness=population[best_mem].fitness;   }   else  {     for(i=0;i<NVARS;i++)       population[worst_mem].gene[i]=population[POPSIZE].gene[i];     population[worst_mem].fitness=population[POPSIZE].fitness;   }} //*************************************************************************** //Select function:Standard proportional selection for maximization problems//incorporating elitist model--makes sure that the best member survives.篩選函數併產生子代//*************************************************************************** void select(void) {   int mem,i,j;   double sum=0;   double p;   for(mem=0;mem<POPSIZE;mem++)//所有適應值求和  {     sum+=population[mem].fitness;   }   for(mem=0;mem<POPSIZE;mem++)   {    population[mem].rfitness=population[mem].fitness/sum;//個人認為還不如建一個種群類 把sum看成類成員  }  population[0].cfitness=population[0].rfitness;  for(mem=1;mem<POPSIZE;mem++)  {    population[mem].cfitness=population[mem-1].cfitness+population[mem].rfitness;  }   for(i=0;i<POPSIZE;i++)  {     p=rand()%1000/1000.0;     if(p<population[0].cfitness)    {       newpopulation[i]=population[0];     }     else    {      for(j=0;j<POPSIZE;j++)         if(p>=population[j].cfitness&&p<population[j+1].cfitness)           newpopulation[i]=population[j+1];     }   }   for(i=0;i<POPSIZE;i++)//子代變父代     population[i]=newpopulation[i];}//*************************************************************************** //Crossover:performs crossover of the selected parents. //***************************************************************************void Xover(int one,int two)//基因重組函數{   int i;  int point;  if(NVARS>1)  {     if(NVARS==2)      point=1;    else      point=(rand()%(NVARS-1))+1;//兩個都重組嗎?    for(i=0;i<point;i++)//只有第一個基因發生重組有待改進      swap(&population[one].gene[i],&population[two].gene[i]);   } }//***************************************************************************//Swapp: a swap procedure the helps in swappling 2 variables//*************************************************************************** void swap(double *x,double *y) {  double temp;  temp=*x;  *x=*y;  *y=temp;} //*************************************************************************** //Crossover function:select two parents that take part in the crossover. //Implements a single point corssover.雜交函數 //***************************************************************************void crossover(void) {   int mem,one;   int first=0;   double x;  for(mem=0;mem<POPSIZE;++mem)  {    x=rand()%1000/1000.0;    if(x<PXOVER)     {       ++first;      if(first%2==0)//選擇雜交的個體對 雜交有待改進 事實上往往是強者與強者雜交 這裡沒有考慮雌雄與雜交對象的選擇        Xover(one,mem);      else         one=mem; }  } }//*************************************************************************** //Mutation function:Random uniform mutation.a variable selected for mutation //變異函數 事實基因的變異往往具有某種局部性 //is replaced by a random value between lower and upper bounds of the variables. //*************************************************************************** void mutate(void) {   int i,j;   double lbound,hbound;   double x;   for(i=0;i<POPSIZE;i++)     for(j=0;j<NVARS;j++)     {       x=rand()%1000/1000.0;       if(x<PMUTATION)      {         lbound=population[i].lower[j];         hbound=population[i].upper[j];         population[i].gene[j]=randval(lbound,hbound);       }     } }//*************************************************************************** //Report function:Reports progress of the simulation. //*************************************************************************** double report(void) {  int i;  double best_val;//種群內最優適應值  double avg;//平均個體適應值   //double stddev;  double sum_square;//種群內個體適應值平方和  //double square_sum;  double sum;//種群適應值  sum=0.0;  sum_square=0.0;  for(i=0;i<POPSIZE;i++)   {     sum+=population[i].fitness;     sum_square+=population[i].fitness*population[i].fitness;   }  avg=sum/(double)POPSIZE;   //square_sum=avg*avg*(double)POPSIZE;   //stddev=sqrt((sum_square-square_sum)/(POPSIZE-1));  best_val=population[POPSIZE].fitness;  fprintf(galog,"%6d %6.3f %6.3f %6.3f %6.3f %6.3f\n",generation,best_val,population[POPSIZE].gene[0],population[POPSIZE].gene[1],avg,sum);  return avg; } //***************************************************************************//main function:Each generation involves selecting the best members,performing //crossover & mutation and then evaluating the resulting population,until the//terminating condition is satisfied. //*************************************************************************** void main(void) {   int i;   double temp;   double temp1;   if((galog=fopen("data.txt","w"))==NULL)  {    exit(1);   }  generation=1;  srand(time(NULL));//產生隨機數  fprintf(galog,"number value  x1   x2   avg   sum_value\n");  printf("generation best average standard\n");  initialize();  evaluate();  keep_the_best();  temp=report();//記錄,暫存上一代個體平均適應值     do   {           select();//篩選     crossover();//雜交     mutate();//變異     evaluate();//評價     keep_the_best();//elitist();     temp1=report();     diff=fabs(temp-temp1);//求浮點數x的絕對值     temp=temp1;     generation++;   }while(generation<MAXGENS&&diff>=STOP);   //fprintf(galog,"\n\n Simulation completed\n");   //fprintf(galog,"\n Best member:\n");   printf("\nBest member:\ngeneration:%d\n",generation);   for(i=0;i<NVARS;i++)   {     //fprintf(galog,"\n var(%d)=%3.3f",i,population[POPSIZE].gene[i]);     printf("X%d=%3.3f\n",i,population[POPSIZE].gene[i]);   }   //fprintf(galog,"\n\n Best fitness=%3.3f",population[POPSIZE].fitness);   fclose(galog);   printf("\nBest fitness=%3.3f\n",population[POPSIZE].fitness); }

感興趣的讀者可以動手測試一下代碼，希望對大家學習C++演算法能有所協助。