一个简单实用的遗传算法c程序(15页).doc

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1、-一个简单实用的遗传算法c程序-第 15 页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 State University)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

2、的适应值之间没有区别。该系统使用比率选择、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,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；系统产生的输出文件为。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 implementation where the */* eval

3、uation function takes positive values only and the */* fitness of an individual is the same as the value of the */* objective function */#include #include #include /* Change any of these parameters to match your needs */#define POPSIZE 50 /* population size */#define MAXGENS 1000 /* max. number of g

4、enerations */#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 *galog; /* an output file *

5、/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; /* relative fitn

6、ess 相对适应度*/ double cfitness; /* cumulative fitness 累计适应度*/struct genotype populationPOPSIZE+1; /* population */struct genotype newpopulationPOPSIZE+1; /* new population; */ /* replaces the */ /* old generation */* Declaration of procedures used by this genetic algorithm */void initialize(void);doubl

7、e 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 the variables

8、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 */* input file gadata.txt. It randomly generates values */* between these bounds for each gene of each genotype in the */* population. The for

9、mat 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);/* initialize variables within

10、 the bounds */for (i = 0; i NVARS; i+) fscanf(infile, %lf,&lbound); fscanf(infile, %lf,&ubound); for (j = 0; j POPSIZE; j+) populationj.fitness = 0; populationj.rfitness = 0; populationj.cfitness = 0; populationj.loweri = lbound; populationj.upperi= ubound; populationj.genei = randval(populationj.lo

11、weri, 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);/* Evaluation function: This takes a user defined function. */* Each time this is

12、 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;/* Keep_the_best function: This f

13、unction keeps track of the */* 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; mem populationPOPSIZE.fitness) cur_best

14、= 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;/* Elitist function: The best member of the previous generation */* is stored as the last in the a

15、rray. 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 fitness values */int best_mem, worst_mem; /* in

16、dexes 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; if (populationi+1.fitness = worst) worst = populationi+1.fitness; worst_mem = i + 1; else if (pop

17、ulationi.fitness = best) best = populationi+1.fitness; best_mem = i + 1;/* 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 the */* worst individual from the current population

18、 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;else for (i = 0; i NVARS; i+) populationworst_mem.genei = populationPOPSIZE.

19、genei; populationworst_mem.fitness = populationPOPSIZE.fitness;/* Selection function: Standard proportional selection for */* maximization problems incorporating elitist model - makes */* sure that the best member survives */void select(void)int mem, i, j, k;double sum = 0;double p;/* find total fit

20、ness 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;/* calculate cumulative fitness */for (mem = 1; mem P

21、OPSIZE; mem+) 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 = population0; else for (j = 0; j = populationj.cfitness & pp

22、opulationj+1.cfitness) newpopulationi = populationj+1;/* once a new population is created, copy it back */for (i = 0; i POPSIZE; i+) populationi = newpopulationi; /* Crossover selection: selects two parents that take part in */* the crossover. Implements a single point crossover */void crossover(voi

23、d)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; else point = (rand() % (NVARS - 1) + 1; for (i = 0; i point; i+) swap(&populationone.genei, &populationtwo.genei);/* Swap: A

24、 swap procedure that helps in swapping 2 variables */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 between lower and */* upper bounds of this variable */void mutate(void)

25、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) /* find the bounds on the variable to be mutated */ lbound = populationi.lowerj; hbound = populationi.upperj; populationi.genej = randval(lbound, hbound);/* Report fun

26、ction: 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; /* std. deviation of population fitness */double sum_square; /* sum of

27、 square for std. calc */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.fitness * populationi.fitness;avg = sum/(double)POPSIZE;square_sum = avg *

28、 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 selecting the best */* members, performing crossover & mutation and then

29、 */* evaluating the resulting population, until the terminating */* condition is satisfied */void main(void)int i;if (galog = fopen(galog.txt,w)=NULL) exit(1);generation = 0;fprintf(galog, n generation best average standard n);fprintf(galog, number value fitness deviation n);initialize();evaluate();

30、keep_the_best();while(generationMAXGENS) generation+; select(); crossover(); mutate(); report(); evaluate(); elitist();fprintf(galog,nn Simulation completedn);fprintf(galog,n Best member: n);for (i = 0; i NVARS; i+) fprintf (galog,n var(%d) = %3.3f,i,populationPOPSIZE.genei);fprintf(galog,nn Best fi

31、tness = %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 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。