c++遗传算法库（c++实现遗传算法）

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：

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 #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 %6.3f\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));//产生随机数 fprintf(galog,"number value x1 x2 avg sum_value\n"); printf("generation best average standard\n"); 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,"\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); }