本文所述为C++实现的遗传算法的类文件实例。一般来说遗传算法可以解决许多问题，希望本文所述的C++遗传算法类文件，可帮助你解决更多问题，并且代码中为了便于读者更好的理解，而加入了丰富的注释内容，是新手学习遗传算法不可多得的参考代码。

具体代码如下所示：

#include "stdafx.h" #include<iostream> #include<cstdio> #include<cstdlib> #include<cmath> #include<ctime>//把日期和时间转换为字符串 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 0.001 int generation; //current generation no int cur_best; //best individual double diff; FILE *galog; //an output file struct genotype { double gene[NVARS]; //a string of variables基因变量 double upper[NVARS]; //individual's variables upper bound 基因变量取值上确界 double lower[NVARS]; //individual's batiables lower bound 基因变量取值下确界 double fitness; //individual's fitness个体适应值 double rfitness; //relative fitness个体适应值占种群适应值比例 double cfitness; //curmulation fitness个体适应值的累加比例 }; struct genotype population[POPSIZE+1]; //population 当前种群 population[POPSIZE]用于存放个体最优值并假设最优个体能存活下去 //在某些遗传算法中最优值个体并不一定能够存活下去 struct genotype newpopulation[POPSIZE+1]; //new population replaces the old generation 子种群 /*Declaration of procedures used by the gentic algorithm*/ void initialize(void); //初始化函数 double randval(double,double); //随机函数 double funtion(double x1,double x2); //目标函数 void evaluate(void); //评价函数 void keep_the_best(void); //保留最优个体 void elitist(void); //当前种群与子代种群最优值比较 void select(void); void crossover(void); //基因重组函数 void swap(double *,double *); //交换函数 void mutate(void); //基因突变函数 double report(void); //数据记录函数 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; } //目标函数 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.评价函数给出个体适应值 //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]);//将目标函数值作为适应值 } } //*************************************************************************************** //Keep_the_best function:This function keeps track of the best member of the population. //找出种群中的个体最优值并将其移动到最后 //*************************************************************************************** void keep_the_best() { int mem; int i; cur_best=0; for(mem=0;mem<POPSIZE;mem++)//找出最高适应值个体 { if(population[mem].fitness<population[cur_best].fitness) { cur_best=mem; } } //将最优个体复制至population[POSIZE] if(population[cur_best].fitness<=population[POPSIZE].fitness||population[POPSIZE].fitness<1)//防止出现种群基因退化 故保留历史最优个体 { 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.防止种群最优值退化 //*************************************************************************** void elitist() { int i; double best,worst;//适应值 int best_mem,worst_mem;//序号 best_mem=worst_mem=0; best=population[best_mem].fitness;//最高适应值初始化 worst=population[worst_mem].fitness;//最低适应值初始化 for(i=1;i<POPSIZE;i++)//找出最高和最低适应值 算法有待改进 { 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)//赋值 { 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.筛选函数并产生子代 //*************************************************************************** void select(void) { int mem,i,j; double sum=0; double p; for(mem=0;mem<POPSIZE;mem++)//所有适应值求和 { sum+=population[mem].fitness; } for(mem=0;mem<POPSIZE;mem++) { population[mem].rfitness=population[mem].fitness/sum;//个人认为还不如建一个种群类 把sum看成类成员 } 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++)//子代变父代 population[i]=newpopulation[i]; } //*************************************************************************** //Crossover:performs crossover of the selected parents. //*************************************************************************** void Xover(int one,int two)//基因重组函数 { int i; int point; if(NVARS>1) { if(NVARS==2) point=1; else point=(rand()%(NVARS-1))+1;//两个都重组吗? for(i=0;i<point;i++)//只有第一个基因发生重组有待改进 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.杂交函数 //*************************************************************************** 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)//选择杂交的个体对 杂交有待改进 事实上往往是强者与强者杂交 这里没有考虑雌雄与杂交对象的选择 Xover(one,mem); else one=mem; } } } //*************************************************************************** //Mutation function:Random uniform mutation.a variable selected for mutation //变异函数 事实基因的变异往往具有某种局部性 //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;//种群内最优适应值 double avg;//平均个体适应值 //double stddev; double sum_square;//种群内个体适应值平方和 //double square_sum; double sum;//种群适应值 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 %6.3fn",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));//产生随机数 fprintf(galog,"number value x1 x2 avg sum_valuen"); printf("generation best average standardn"); initialize(); evaluate(); keep_the_best(); temp=report();//记录,暂存上一代个体平均适应值 do { select();//筛选 crossover();//杂交 mutate();//变异 evaluate();//评价 keep_the_best();//elitist(); temp1=report(); diff=fabs(temp-temp1);//求浮点数x的绝对值 temp=temp1; generation++; }while(generation<MAXGENS&&diff>=STOP); //fprintf(galog,"nn Simulation completedn"); //fprintf(galog,"n Best member:n"); printf("nBest member:ngeneration:%dn",generation); for(i=0;i<NVARS;i++) { //fprintf(galog,"n var(%d)=%3.3f",i,population[POPSIZE].gene[i]); printf("X%d=%3.3fn",i,population[POPSIZE].gene[i]); } //fprintf(galog,"nn Best fitness=%3.3f",population[POPSIZE].fitness); fclose(galog); printf("nBest fitness=%3.3fn",population[POPSIZE].fitness); }

感兴趣的读者可以动手测试一下代码，希望对大家学习C++算法能有所帮助。