本文实例讲述了C++实现简单遗传算法。分享给大家供大家参考。具体实现方法如下：
|
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
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
|
// CMVSOGA.h : main header file for the CMVSOGA.cpp//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// #if !defined(AFX_CMVSOGA_H__45BECA_61EB_4A0E_9746_9A94D1CCF767__INCLUDED_)#define AFX_CMVSOGA_H__45BECA_61EB_4A0E_9746_9A94D1CCF767__INCLUDED_ #if _MSC_VER > 1000#pragma once#endif // _MSC_VER > 1000#include "Afxtempl.h"#define variablenum 14class CMVSOGA{public: CMVSOGA(); ~CMVSOGA(); void selectionoperator(); void crossoveroperator(); void mutationoperator(); void initialpopulation(int, int ,double ,double,double *,double *); //种群初始化 void generatenextpopulation(); //生成下一代种群 void evaluatepopulation(); //评价个体，求最佳个体 void calculateobjectvalue(); //计算目标函数值 void calculatefitnessvalue(); //计算适应度函数值 void findbestandworstindividual(); //寻找最佳个体和最差个体 void performevolution(); void GetResult(double *); void GetPopData(CList <double,double>&); void SetFitnessData(CList <double,double>&,CList <double,double>&,CList <double,double>&);private: struct individual { double chromosome[variablenum]; //染色体编码长度应该为变量的个数 double value; double fitness; //适应度 }; double variabletop[variablenum]; //变量值 double variablebottom[variablenum]; //变量值 int popsize; //种群大小// int generation; //世代数 int best_index; int worst_index; double crossoverrate; //交叉率 double mutationrate; //变异率 int maxgeneration; //最大世代数 struct individual bestindividual; //最佳个体 struct individual worstindividual; //最差个体 struct individual current; //当前个体 struct individual current1; //当前个体 struct individual currentbest; //当前最佳个体 CList <struct individual,struct individual &> population; //种群 CList <struct individual,struct individual &> newpopulation; //新种群 CList <double,double> cfitness; //存储适应度值 //怎样使链表的数据是一个结构体????主要是想把种群作成链表。节省空间。};#endif 执行文件： // CMVSOGA.cpp : implementation file// #include "stdafx.h"//#include "vld.h"#include "CMVSOGA.h"#include "math.h"#include "stdlib.h" #ifdef _DEBUG#define new DEBUG_NEW#undef THIS_FILEstatic char THIS_FILE[] = __FILE__;#endif/////////////////////////////////////////////////////////////////////////////// CMVSOGA.cppCMVSOGA::CMVSOGA(){ best_index=0; worst_index=0; crossoverrate=0; //交叉率 mutationrate=0; //变异率 maxgeneration=0;}CMVSOGA::~CMVSOGA(){ best_index=0; worst_index=0; crossoverrate=0; //交叉率 mutationrate=0; //变异率 maxgeneration=0; population.RemoveAll(); //种群 newpopulation.RemoveAll(); //新种群 cfitness.RemoveAll(); }void CMVSOGA::initialpopulation(int ps, int gen ,double cr ,double mr,double *xtop,double *xbottom) //第一步，初始化。{ //应该采用一定的策略来保证遗传算法的初始化合理，采用产生正态分布随机数初始化？选定中心点为多少？ int i,j; popsize=ps; maxgeneration=gen; crossoverrate=cr; mutationrate =mr; for (i=0;i<variablenum;i++) { variabletop[i] =xtop[i]; variablebottom[i] =xbottom[i]; } //srand( (unsigned)time( NULL ) ); //寻找一个真正的随机数生成函数。 for(i=0;i<popsize;i++) { for (j=0;j<variablenum ;j++) { current.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j]; } current.fitness=0; current.value=0; population.InsertAfter(population.FindIndex(i),current);//除了初始化使用insertafter外,其他的用setat命令。 }}void CMVSOGA::generatenextpopulation()//第三步，生成下一代。{ //srand( (unsigned)time( NULL ) ); selectionoperator(); crossoveroperator(); mutationoperator();}//void CMVSOGA::evaluatepopulation() //第二步，评价个体，求最佳个体//{// calculateobjectvalue();// calculatefitnessvalue(); //在此步中因该按适应度值进行排序.链表的排序.// findbestandworstindividual();//}void CMVSOGA:: calculateobjectvalue() //计算函数值，应该由外部函数实现。主要因为目标函数很复杂。{ int i,j; double x[variablenum]; for (i=0; i<popsize; i++) { current=population.GetAt(population.FindIndex(i)); current.value=0; //使用外部函数进行，在此只做结果的传递。 for (j=0;j<variablenum;j++) { x[j]=current.chromosome[j]; current.value=current.value+(j+1)*pow(x[j],4); } ////使用外部函数进行，在此只做结果的传递。 population.SetAt(population.FindIndex(i),current); }} void CMVSOGA::mutationoperator() //对于浮点数编码，变异算子的选择具有决定意义。 //需要guass正态分布函数，生成方差为sigma，均值为浮点数编码值c。{// srand((unsigned int) time (NULL)); int i,j; double r1,r2,p,sigma;//sigma高斯变异参数 for (i=0;i<popsize;i++) { current=population.GetAt(population.FindIndex(i)); //生成均值为current.chromosome，方差为sigma的高斯分布数 for(j=0; j<variablenum; j++) { r1 = double(rand()%10001)/10000; r2 = double(rand()%10001)/10000; p = double(rand()%10000)/10000; if(p<mutationrate) { double sign; sign=rand()%2; sigma=0.01*(variabletop[j]-variablebottom [j]); //高斯变异 if(sign) { current.chromosome[j] = (current.chromosome[j] + sigma*sqrt(-2*log(r1)/0.4323)*sin(2*3.1415926*r2)); } else { current.chromosome[j] = (current.chromosome[j] - sigma*sqrt(-2*log(r1)/0.4323)*sin(2*3.1415926*r2)); } if (current.chromosome[j]>variabletop[j]) { current.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j]; } if (current.chromosome[j]<variablebottom [j]) { current.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j]; } } } population.SetAt(population.FindIndex(i),current); }}void CMVSOGA::selectionoperator() //从当前个体中按概率选择新种群,应该加一个复制选择,提高种群的平均适应度{ int i,j,pindex=0; double p,pc,sum; i=0; j=0; pindex=0; p=0; pc=0; sum=0.001; newpopulation.RemoveAll(); cfitness.RemoveAll(); //链表排序// population.SetAt (population.FindIndex(0),current); //多余代码 for (i=1;i<popsize;i++) { current=population.GetAt(population.FindIndex(i)); for(j=0;j<i;j++) //从小到大用before排列。 { current1=population.GetAt(population.FindIndex(j));//临时借用变量 if(current.fitness<=current1.fitness) { population.InsertBefore(population.FindIndex(j),current); population.RemoveAt(population.FindIndex(i+1)); break; } }// m=population.GetCount(); } //链表排序 for(i=0;i<popsize;i++)//求适应度总值，以便归一化,是已经排序好的链。 { current=population.GetAt(population.FindIndex(i)); //取出来的值出现问题. sum+=current.fitness; } for(i=0;i<popsize; i++)//归一化 { current=population.GetAt(population.FindIndex(i)); //population 有值,为什么取出来的不正确呢?? current.fitness=current.fitness/sum; cfitness.InsertAfter (cfitness .FindIndex(i),current.fitness); } for(i=1;i<popsize; i++)//概率值从小到大; { current.fitness=cfitness.GetAt (cfitness.FindIndex(i-1)) +cfitness.GetAt(cfitness.FindIndex(i)); //归一化 cfitness.SetAt (cfitness .FindIndex(i),current.fitness); population.SetAt(population.FindIndex(i),current); } for (i=0;i<popsize;)//轮盘赌概率选择。本段还有问题。 { p=double(rand()%999)/1000+0.0001; //随机生成概率 pindex=0; //遍历索引 pc=cfitness.GetAt(cfitness.FindIndex(1)); //为什么取不到数值???20060910 while(p>=pc&&pindex<popsize) //问题所在。 { pc=cfitness.GetAt(cfitness .FindIndex(pindex)); pindex++; } //必须是从index~popsize，选择高概率的数。即大于概率p的数应该被选择，选择不满则进行下次选择。 for (j=popsize-1;j<pindex&&i<popsize;j--) { newpopulation.InsertAfter (newpopulation.FindIndex(0), population.GetAt (population.FindIndex(j))); i++; } } for(i=0;i<popsize; i++) { population.SetAt (population.FindIndex(i), newpopulation.GetAt (newpopulation.FindIndex(i))); }// j=newpopulation.GetCount();// j=population.GetCount(); newpopulation.RemoveAll();} //current 变化后，以上没有问题了。 void CMVSOGA:: crossoveroperator() //非均匀算术线性交叉，浮点数适用,alpha ,beta是(0，1)之间的随机数 //对种群中两两交叉的个体选择也是随机选择的。也可取beta=1-alpha; //current的变化会有一些改变。{ int i,j; double alpha,beta; CList <int,int> index; int point,temp; double p;// srand( (unsigned)time( NULL ) ); for (i=0;i<popsize;i++)//生成序号 { index.InsertAfter (index.FindIndex(i),i); } for (i=0;i<popsize;i++)//打乱序号 { point=rand()%(popsize-1); temp=index.GetAt(index.FindIndex(i)); index.SetAt(index.FindIndex(i), index.GetAt(index.FindIndex(point))); index.SetAt(index.FindIndex(point),temp); } for (i=0;i<popsize-1;i+=2) {//按顺序序号,按序号选择两个母体进行交叉操作。 p=double(rand()%10000)/10000.0; if (p<crossoverrate) { alpha=double(rand()%10000)/10000.0; beta=double(rand()%10000)/10000.0; current=population.GetAt(population.FindIndex(index.GetAt(index.FindIndex(i)))); current1=population.GetAt(population.FindIndex(index.GetAt(index.FindIndex(i+1))));//临时使用current1代替 for(j=0;j<variablenum;j++) { //交叉 double sign; sign=rand()%2; if(sign) { current.chromosome[j]=(1-alpha)*current.chromosome[j]+ beta*current1.chromosome[j]; } else { current.chromosome[j]=(1-alpha)*current.chromosome[j]- beta*current1.chromosome[j]; } if (current.chromosome[j]>variabletop[j]) //判断是否超界. { current.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j]; } if (current.chromosome[j]<variablebottom [j]) { current.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j]; } if(sign) { current1.chromosome[j]=alpha*current.chromosome[j]+ (1- beta)*current1.chromosome[j]; } else { current1.chromosome[j]=alpha*current.chromosome[j]- (1- beta)*current1.chromosome[j]; } if (current1.chromosome[j]>variabletop[j]) { current1.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j]; } if (current1.chromosome[j]<variablebottom [j]) { current1.chromosome[j]=double(rand()%10000)/10000*(variabletop[j]-variablebottom[j])+variablebottom[j]; } } //回代 } newpopulation.InsertAfter (newpopulation.FindIndex(i),current); newpopulation.InsertAfter (newpopulation.FindIndex(i),current1); } ASSERT(newpopulation.GetCount()==popsize); for (i=0;i<popsize;i++) { population.SetAt (population.FindIndex(i), newpopulation.GetAt (newpopulation.FindIndex(i))); } newpopulation.RemoveAll(); index.RemoveAll();}void CMVSOGA:: findbestandworstindividual( ) { int i; bestindividual=population.GetAt(population.FindIndex(best_index)); worstindividual=population.GetAt(population.FindIndex(worst_index)); for (i=1;i<popsize; i++) { current=population.GetAt(population.FindIndex(i)); if (current.fitness>bestindividual.fitness) { bestindividual=current; best_index=i; } else if (current.fitness<worstindividual.fitness) { worstindividual=current; worst_index=i; } } population.SetAt(population.FindIndex(worst_index), population.GetAt(population.FindIndex(best_index))); //用最好的替代最差的。 if (maxgeneration==0) { currentbest=bestindividual; } else { if(bestindividual.fitness>=currentbest.fitness) { currentbest=bestindividual; } }}void CMVSOGA:: calculatefitnessvalue() //适应度函数值计算，关键是适应度函数的设计 //current变化，这段程序变化较大，特别是排序。{ int i; double temp;//alpha,beta;//适应度函数的尺度变化系数 double cmax=100; for(i=0;i<popsize;i++) { current=population.GetAt(population.FindIndex(i)); if(current.value<cmax) { temp=cmax-current.value; } else { temp=0.0; } /* if((population[i].value+cmin)>0.0) {temp=cmin+population[i].value;} else {temp=0.0; } */ current.fitness=temp; population.SetAt(population.FindIndex(i),current); }}void CMVSOGA:: performevolution() //演示评价结果,有冗余代码,current变化，程序应该改变较大{ if (bestindividual.fitness>currentbest.fitness) { currentbest=population.GetAt(population.FindIndex(best_index)); } else { population.SetAt(population.FindIndex(worst_index),currentbest); }}void CMVSOGA::GetResult(double *Result){ int i; for (i=0;i<variablenum;i++) { Result[i]=currentbest.chromosome[i]; } Result[i]=currentbest.value;} void CMVSOGA::GetPopData(CList <double,double>&PopData) { PopData.RemoveAll(); int i,j; for (i=0;i<popsize;i++) { current=population.GetAt(population.FindIndex(i)); for (j=0;j<variablenum;j++) { PopData.AddTail(current.chromosome[j]); } }}void CMVSOGA::SetFitnessData(CList <double,double>&PopData,CList <double,double>&FitnessData,CList <double,double>&ValueData){ int i,j; for (i=0;i<popsize;i++) { current=population.GetAt(population.FindIndex(i)); //就因为这一句，出现了很大的问题。 for (j=0;j<variablenum;j++) { current.chromosome[j]=PopData.GetAt(PopData.FindIndex(i*variablenum+j)); } current.fitness=FitnessData.GetAt(FitnessData.FindIndex(i)); current.value=ValueData.GetAt(ValueData.FindIndex(i)); population.SetAt(population.FindIndex(i),current); } FitnessData.RemoveAll(); PopData.RemoveAll(); ValueData.RemoveAll();} # re: C++遗传算法源程序/********************************************************************Filename: aiWorld.hPurpose: 遗传算法，花朵演化。Id:Copyright:Licence:*********************************************************************/#ifndef AIWORLD_H_#define AIWORLD_H_ #include <iostream>#include <ctime>#include <cstdlib>#include <cmath> #define kMaxFlowers 10 using std::cout;using std::endl; class ai_World{public:ai_World(){srand(time(0));}~ai_World() {} int temperature[kMaxFlowers]; //温度int water[kMaxFlowers]; //水质int sunlight[kMaxFlowers]; //阳光int nutrient[kMaxFlowers]; //养分int beneficialInsect[kMaxFlowers]; //益虫int harmfulInsect[kMaxFlowers]; //害虫 int currentTemperature;int currentWater;int currentSunlight;int currentNutrient;int currentBeneficialInsect;int currentHarmfulInsect; /**第一代花朵*/void Encode(); /**花朵适合函数*/int Fitness(int flower); /**花朵演化*/void Evolve(); /**返回区间[start, end]的随机数*/inline int tb_Rnd(int start, int end){if (start > end)return 0;else{//srand(time(0));return (rand() % (end + 1) + start);}} /**显示数值*/void show();};// ----------------------------------------------------------------- //void ai_World::Encode()// ----------------------------------------------------------------- // {int i; for (i=0;i<kMaxFlowers;i++){temperature[i]=tb_Rnd(1,75);water[i]=tb_Rnd(1,75);sunlight[i]=tb_Rnd(1,75);nutrient[i]=tb_Rnd(1,75);beneficialInsect[i]=tb_Rnd(1,75);harmfulInsect[i]=tb_Rnd(1,75);} currentTemperature=tb_Rnd(1,75);currentWater=tb_Rnd(1,75);currentSunlight=tb_Rnd(1,75);currentNutrient=tb_Rnd(1,75);currentBeneficialInsect=tb_Rnd(1,75);currentHarmfulInsect=tb_Rnd(1,75); currentTemperature=tb_Rnd(1,75);currentWater=tb_Rnd(1,75);currentSunlight=tb_Rnd(1,75);currentNutrient=tb_Rnd(1,75);currentBeneficialInsect=tb_Rnd(1,75);currentHarmfulInsect=tb_Rnd(1,75); }// ----------------------------------------------------------------- //int ai_World::Fitness(int flower)// ----------------------------------------------------------------- // {int theFitness; theFitness=abs(temperature[flower]-currentTemperature);theFitness=theFitness+abs(water[flower]-currentWater);theFitness=theFitness+abs(sunlight[flower]-currentSunlight);theFitness=theFitness+abs(nutrient[flower]-currentNutrient);theFitness=theFitness+abs(beneficialInsect[flower]-currentBeneficialInsect);theFitness=theFitness+abs(harmfulInsect[flower]-currentHarmfulInsect); return (theFitness); }// ----------------------------------------------------------------- //void ai_World::Evolve()// ----------------------------------------------------------------- // {int fitTemperature[kMaxFlowers];int fitWater[kMaxFlowers];int fitSunlight[kMaxFlowers];int fitNutrient[kMaxFlowers];int fitBeneficialInsect[kMaxFlowers];int fitHarmfulInsect[kMaxFlowers];int fitness[kMaxFlowers];int i;int leastFit=0;int leastFitIndex; for (i=0;i<kMaxFlowers;i++)if (Fitness(i)>leastFit){leastFit=Fitness(i);leastFitIndex=i;} temperature[leastFitIndex]=temperature[tb_Rnd(0,kMaxFlowers - 1)];water[leastFitIndex]=water[tb_Rnd(0,kMaxFlowers - 1)];sunlight[leastFitIndex]=sunlight[tb_Rnd(0,kMaxFlowers - 1)];nutrient[leastFitIndex]=nutrient[tb_Rnd(0,kMaxFlowers - 1)];beneficialInsect[leastFitIndex]=beneficialInsect[tb_Rnd(0,kMaxFlowers - 1)];harmfulInsect[leastFitIndex]=harmfulInsect[tb_Rnd(0,kMaxFlowers - 1)]; for (i=0;i<kMaxFlowers;i++){fitTemperature[i]=temperature[tb_Rnd(0,kMaxFlowers - 1)];fitWater[i]=water[tb_Rnd(0,kMaxFlowers - 1)];fitSunlight[i]=sunlight[tb_Rnd(0,kMaxFlowers - 1)];fitNutrient[i]=nutrient[tb_Rnd(0,kMaxFlowers - 1)];fitBeneficialInsect[i]=beneficialInsect[tb_Rnd(0,kMaxFlowers - 1)];fitHarmfulInsect[i]=harmfulInsect[tb_Rnd(0,kMaxFlowers - 1)];} for (i=0;i<kMaxFlowers;i++){temperature[i]=fitTemperature[i];water[i]=fitWater[i];sunlight[i]=fitSunlight[i];nutrient[i]=fitNutrient[i];beneficialInsect[i]=fitBeneficialInsect[i];harmfulInsect[i]=fitHarmfulInsect[i];} for (i=0;i<kMaxFlowers;i++){if (tb_Rnd(1,100)==1)temperature[i]=tb_Rnd(1,75);if (tb_Rnd(1,100)==1)water[i]=tb_Rnd(1,75);if (tb_Rnd(1,100)==1)sunlight[i]=tb_Rnd(1,75);if (tb_Rnd(1,100)==1)nutrient[i]=tb_Rnd(1,75);if (tb_Rnd(1,100)==1)beneficialInsect[i]=tb_Rnd(1,75);if (tb_Rnd(1,100)==1)harmfulInsect[i]=tb_Rnd(1,75);} }void ai_World::show(){// cout << "/t temperature water sunlight nutrient beneficialInsect harmfulInsect/n";cout << "current/t " << currentTemperature << "/t " << currentWater << "/t ";cout << currentSunlight << "/t " << currentNutrient << "/t ";cout << currentBeneficialInsect << "/t " << currentHarmfulInsect << "/n";for (int i=0;i<kMaxFlowers;i++){cout << "Flower " << i << ": ";cout << temperature[i] << "/t ";cout << water[i] << "/t ";cout << sunlight[i] << "/t ";cout << nutrient[i] << "/t ";cout << beneficialInsect[i] << "/t ";cout << harmfulInsect[i] << "/t ";cout << endl;}}#endif // AIWORLD_H_ //test.cpp#include <iostream>#include "ai_World.h" using namespace std; int main(){ai_World a;a.Encode();// a.show();for (int i = 0; i < 10; i++){cout << "Generation " << i << endl;a.Evolve();a.show();} system("PAUSE");return 0;} |
希望本文所述对大家的C++程序设计有所帮助。
