一个简单实用地遗传算法c程序

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1、实用标准文案一个简单实用的遗传算法c 程序（转载）c+ 2009-07-28 23:09:03阅读 418 评论 0字号：大 中小这是一个非常简单的遗传算法源代码，是由Denis Cormier (North Carolina StateUniversity)开发的， Sita S.Raghavan (University of North Carolina at Charlotte)修正。 代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统

2、使用比率选择、精华模型、单点杂交和均匀变异。如果用Gaussian 变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu, 目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为gadata.txt ；系统产生的输出文件为 galog.txt 。输入的文件由几行组成：数目对应于变量数。且每一行提供次序对应于变量的上下界。 如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。/*/* This is a simple genetic algorithm implemen

3、tation where the */* evaluation function takes positive values only and the*/* fitness of an individual is the same as the value of the*/* objective function*/*/#include #include 文档实用标准文案#include /* Change any of these parameters to match your needs */#define POPSIZE 50/* population size */#define M

4、AXGENS 1000/* max. number of generations */#define NVARS 3/* no. of problem variables */#define PXOVER 0.8/* probability of crossover */#define PMUTATION 0.15/* probability of mutation */#define TRUE 1#define FALSE 0int generation;/* current generation no. */int cur_best;/* best individual */FILE *g

5、alog;/* an output file */struct genotype /* genotype (GT), a member of the population */double geneNVARS;/* a string of variables一个变量字符串*/double fitness;/* GTs fitness适应度 */double upperNVARS;/* GTs variables upper bound变量的上限 */double lowerNVARS;/* GTs variables lower bound变量的下限*/double rfitness;/* r

6、elative fitness相对适应度 */double cfitness;/* cumulative fitness累计适应度 */;文档实用标准文案struct genotype populationPOPSIZE+1;/* population */struct genotype newpopulationPOPSIZE+1; /* new population; */* replaces the */* old generation */* Declaration of procedures used by this genetic algorithm */void initiali

7、ze(void);double randval(double, double);void evaluate(void);void keep_the_best(void);void elitist(void);void select(void);void crossover(void);void Xover(int,int);void swap(double *, double *);void mutate(void);void report(void);/*/* Initialization function: Initializes the values of genes*/* within

8、 the variables bounds. It also initializes (to zero) */* all fitness values for each member of the population. It*/* reads upper and lower bounds of each variable from the*/文档实用标准文案/* input file gadata.txt. It randomly generates values*/* between these bounds for each gene of each genotype in the */

9、* population. The format of the input file gadata.txt is*/* var1_lower_bound var1_upper bound*/* var2_lower_bound var2_upper bound .*/*/void initialize(void)FILE *infile;int i, j;double lbound, ubound;if (infile = fopen(gadata.txt,r)=NULL)fprintf(galog,nCannot open input file!n);exit(1);/* initializ

10、e variables within the bounds */for (i = 0; i NVARS; i+)fscanf(infile, %lf,&lbound);fscanf(infile, %lf,&ubound);文档实用标准文案for (j = 0; j POPSIZE; j+)populationj.fitness = 0;populationj.rfitness = 0;populationj.cfitness = 0;populationj.loweri = lbound;populationj.upperi= ubound;populationj.genei = randv

11、al(populationj.loweri,populationj.upperi);fclose(infile);/*/* Random value generator: Generates a value within bounds */*/double randval(double low, double high)double val;val = (double)(rand()%1000)/1000.0)*(high - low) + low;return(val);文档实用标准文案/*/* Evaluation function: This takes a user defined f

12、unction. */* Each time this is changed, the code has to be recompiled. */* The current function is: x12-x1*x2+x3*/*/void evaluate(void)int mem;int i;double xNVARS+1;for (mem = 0; mem POPSIZE; mem+)for (i = 0; i NVARS; i+)xi+1 = populationmem.genei;populationmem.fitness = (x1*x1) - (x1*x2) + x3;/*/*

13、Keep_the_best function: This function keeps track of the*/文档实用标准文案/* best member of the population. Note that the last entry in */* the array Population holds a copy of the best individual*/*/void keep_the_best()int mem;int i;cur_best = 0; /* stores the index of the best individual */for (mem = 0; m

14、em populationPOPSIZE.fitness)cur_best = mem;populationPOPSIZE.fitness = populationmem.fitness;/* once the best member in the population is found, copy the genes */for (i = 0; i NVARS; i+)populationPOPSIZE.genei = populationcur_best.genei;/*/文档实用标准文案/* Elitist function: The best member of the previou

15、s generation */* is stored as the last in the array. If the best member of*/* the current generation is worse then the best member of the */* previous generation, the latter one would replace the worst */* member of the current population*/*/void elitist()int i;double best, worst;/* best and worst f

16、itness values */int best_mem, worst_mem; /* indexes of the best and worst member */best = population0.fitness;worst = population0.fitness;for (i = 0; i populationi+1.fitness)if (populationi.fitness = best)best = populationi.fitness;best_mem = i;文档实用标准文案if (populationi+1.fitness = worst)worst = popul

17、ationi+1.fitness;worst_mem = i + 1;elseif (populationi.fitness = best)best = populationi+1.fitness;best_mem = i + 1;文档实用标准文案/* if best individual from the new population is better than */* the best individual from the previous population, then*/* copy the best from the new population; else replace t

18、he*/* worst individual from the current population with the*/* best one from the previous generation*/if (best = populationPOPSIZE.fitness)for (i = 0; i NVARS; i+)populationPOPSIZE.genei = populationbest_mem.genei;populationPOPSIZE.fitness = populationbest_mem.fitness;elsefor (i = 0; i NVARS; i+)pop

19、ulationworst_mem.genei = populationPOPSIZE.genei;populationworst_mem.fitness = populationPOPSIZE.fitness;/*/* Selection function: Standard proportional selection for*/* maximization problems incorporating elitist model - makes */文档实用标准文案/* sure that the best member survives*/*/void select(void)int m

20、em, i, j, k;double sum = 0;double p;/* find total fitness of the population */for (mem = 0; mem POPSIZE; mem+)sum += populationmem.fitness;/* calculate relative fitness */for (mem = 0; mem POPSIZE; mem+)populationmem.rfitness = populationmem.fitness/sum;population0.cfitness = population0.rfitness;/*

21、 calculate cumulative fitness */for (mem = 1; mem POPSIZE; mem+)文档实用标准文案populationmem.cfitness = populationmem-1.cfitness +populationmem.rfitness;/* finally select survivors using cumulative fitness. */for (i = 0; i POPSIZE; i+)p = rand()%1000/1000.0;if (p population0.cfitness)newpopulationi = popul

22、ation0;elsefor (j = 0; j = populationj.cfitness &ppopulationj+1.cfitness)newpopulationi = populationj+1;/* once a new population is created, copy it back */for (i = 0; i POPSIZE; i+)populationi = newpopulationi;文档实用标准文案/*/* Crossover selection: selects two parents that take part in */* the crossover

23、. Implements a single point crossover*/*/void crossover(void)int i, mem, one;int first = 0; /* count of the number of members chosen */double x;for (mem = 0; mem POPSIZE; +mem)x = rand()%1000/1000.0;if (x 1)if(NVARS = 2)point = 1;elsepoint = (rand() % (NVARS - 1) + 1;for (i = 0; i point; i+)swap(&po

24、pulationone.genei, &populationtwo.genei);/*/* Swap: A swap procedure that helps in swapping 2 variables */文档实用标准文案/*/void swap(double *x, double *y)double temp;temp = *x;*x = *y;*y = temp;/*/* Mutation: Random uniform mutation. A variable selected for */* mutation is replaced by a random value betwe

25、en lower and*/* upper bounds of this variable*/*/void mutate(void)int i, j;double lbound, hbound;double x;for (i = 0; i POPSIZE; i+)for (j = 0; j NVARS; j+)文档实用标准文案x = rand()%1000/1000.0;if (x PMUTATION)/* find the bounds on the variable to be mutated */lbound = populationi.lowerj;hbound = populatio

26、ni.upperj;populationi.genej = randval(lbound, hbound);/*/* Report function: Reports progress of the simulation. Data*/* dumped into the output file are separated by commas*/*/void report(void)int i;double best_val;/* best population fitness */double avg;/* avg population fitness */double stddev;/* s

27、td. deviation of population fitness */double sum_square;/* sum of square for std. calc */文档实用标准文案double square_sum;/* square of sum for std. calc */double sum;/* total population fitness */sum = 0.0;sum_square = 0.0;for (i = 0; i POPSIZE; i+)sum += populationi.fitness;sum_square += populationi.fitne

28、ss * populationi.fitness;avg = sum/(double)POPSIZE;square_sum = avg * avg * POPSIZE;stddev = sqrt(sum_square - square_sum)/(POPSIZE - 1);best_val = populationPOPSIZE.fitness;fprintf(galog, n%5d,%6.3f, %6.3f, %6.3f nn, generation,best_val, avg, stddev);/*/* Main function: Each generation involves sel

29、ecting the best */* members, performing crossover & mutation and then*/* evaluating the resulting population, until the terminating */* condition is satisfied*/文档实用标准文案/*/void main(void)int i;if (galog = fopen(galog.txt,w)=NULL)exit(1);generation = 0;fprintf(galog, n generation best average standard

30、 n);fprintf(galog, numbervalue fitness deviation n);initialize();evaluate();keep_the_best();while(generationMAXGENS)generation+;select();crossover();mutate();report();文档实用标准文案evaluate();elitist();fprintf(galog,nn Simulation completedn);fprintf(galog,n Best member: n);for (i = 0; i NVARS; i+)fprintf

31、(galog,n var(%d) = %3.3f,i,populationPOPSIZE.genei);fprintf(galog,nn Best fitness = %3.3f,populationPOPSIZE.fitness);fclose(galog);printf(Successn);/*/链接库文件？这个简单一般的第三方库文件有2 种提供方式1.lib静态库， 这样必须在工程设置里面添加。比如可以在项目的“属性”配置对话框里面的，连接器输入。选择“附加依赖项” ，添加进去那个 lib 文件，（注意最好是将此lib拷入工程目录下， 或者设置“附加包含目录” 。或添加 #pragma

32、comment(lib ，“ my.lib)的方式设置库依赖2.dll动态库，有 2 种添加方法，一种是静态的，一种是动态地。动态的就是使用LoadLibrary,GetProcAddress, FreeLibrary, 这 3 个函数，一个是装入dll库，一个是取库中导出函数地址，最后是用完了释放库。用法比较简单，看MSDN的说明就会了。静态的装入必须要提供dll 的 lib 文件（类似于上面的 lib 静态库，但这个lib只是 dll的导出头，具体的功能实现还是在 dll 中，类似于 h 文件），如果未提供，可以使用implib.exe在命令行导出 （这个教老，不过够用，在命令行执行，具体用/? 看帮助）。上面 2种方法都最好有配套的头文件，如果导出有类或者变量的话不知道头文件说明是无法使用的，如果对方提供了导出部分的文档，也可以手工编写头文件。文档