简单的遗传算法源代码

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　　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。

　　

　　#include

　　#define POPSIZE 50

　　#define MAXGENS 1000

　　#define NVARS 3

　　#define PXOVER 0.8

　　#define PMUTATION 0.15

　　#define TRUE 1

　　#define FALSE 0

　　int generation;

　　int cur_best;

　　FILE *galog;

　　struct genotype

　　{

　　double gene[NVARS];

　　double fitness;

　　double upper[NVARS];

　　double lower[NVARS];

　　double rfitness;

　　double cfitness;

　　};

　　struct genotype population[POPSIZE+1];

　　struct genotype newpopulation[POPSIZE+1];

　　void initialize(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);

　　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);

　　}

　　for (i = 0; i

　　fscanf(infile, "%lf",&lbound);

　　fscanf(infile, "%lf",&ubound);

　　for (j = 0; j

　　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]);

　　fclose(infile);

　　double randval(double low, double high)

　　double val;

　　val = ((double)(rand()%1000)/1000.0)*(high - low) + low;

　　return(val);

　　void evaluate(void)

　　int mem;

　　int i;

　　double x[NVARS+1];

　　for (mem = 0; mem

　　x[i+1] = population[mem].gene[i];

　　population[mem].fitness = (x[1]*x[1]) - (x[1]*x[2]) + x[3];

　　void keep_the_best()

　　cur_best = 0;

　　if (population[mem].fitness >population[POPSIZE].fitness)

　　cur_best = mem;

　　population[POPSIZE].fitness = population[mem].fitness;

　　population[POPSIZE].gene[i] = population[cur_best].gene[i];

　　void elitist()

　　double best, worst;

　　int best_mem, worst_mem;

　　best = population[0].fitness;

　　worst = population[0].fitness;

　　if(population[i].fitness >population[i+1].fitness)

　　if (population[i].fitness >= best)

　　best = population[i].fitness;

　　best_mem = i;

　　if (population[i+1].fitness <= worst)

　　worst = population[i+1].fitness;

　　worst_mem = i + 1;

　　else

　　if (population[i].fitness <= worst)

　　worst = population[i].fitness;

　　worst_mem = i;

　　if (population[i+1].fitness >= best)

　　best = population[i+1].fitness;

　　best_mem = i + 1;

　　if (best >= population[POPSIZE].fitness)

　　population[POPSIZE].gene[i] = population[best_mem].gene[i];

　　population[POPSIZE].fitness = population[best_mem].fitness;

　　population[worst_mem].gene[i] = population[POPSIZE].gene[i];

　　population[worst_mem].fitness = population[POPSIZE].fitness;

　　void select(void)

　　int mem, i, j, k;

　　double sum = 0;

　　double p;

　　sum += population[mem].fitness;

　　population[mem].rfitness = population[mem].fitness/sum;

　　population[0].cfitness = population[0].rfitness;

　　for (mem = 1; mem

　　population[mem].cfitness = population[mem-1].cfitness +

　　population[mem].rfitness;

　　p = rand()%1000/1000.0;

　　if (p

　　newpopulation[i] = population[0];

　　if (p >= population[j].cfitness &&

　　p

　　newpopulation[i] = population[j+1];

　　population[i] = newpopulation[i];

　　void crossover(void)

　　int i, mem, one;

　　int first = 0;

　　double x;

　　x = rand()%1000/1000.0;

　　if (x

　　++first;

　　if (first % 2 == 0)

　　Xover(one, mem);

　　one = mem;

　　void Xover(int one, int two)

　　int point;

　　if(NVARS >1)

　　if(NVARS == 2)

　　point = 1;

　　point = (rand() % (NVARS - 1)) + 1;

　　swap(&population[one].gene[i], &population[two].gene[i]);

　　void swap(double *x, double *y)

　　double temp;

　　temp = *x;

　　*x = *y;

　　*y = temp;

　　void mutate(void)

　　double lbound, hbound;

　　lbound = population[i].lower[j];

　　hbound = population[i].upper[j];

　　population[i].gene[j] = randval(lbound, hbound);

　　void report(void)

　　double best_val;

　　double avg;

　　double stddev;

　　double sum_square;

　　double square_sum;

　　double sum;

　　sum = 0.0;

　　sum_square = 0.0;

　　sum += population[i].fitness;

　　sum_square += population[i].fitness * population[i].fitness;

　　avg = sum/(double)POPSIZE;

　　square_sum = avg * avg * POPSIZE;

　　stddev = sqrt((sum_square - square_sum)/(POPSIZE - 1));

　　best_val = population[POPSIZE].fitness;

　　fprintf(galog, "\n%5d, %6.3f, %6.3f, %6.3f \n\n", generation,

　　best_val, avg, stddev);

　　void main(void)

　　if ((galog = fopen("galog.txt","w"))==NULL)

　　generation = 0;

　　fprintf(galog, "\n generation best average standard \n");

　　fprintf(galog, " number value fitness deviation \n");

　　initialize();

　　evaluate();

　　keep_the_best();

　　while(generation

　　generation++;

　　select();

　　crossover();

　　mutate();

　　report();

　　elitist();

　　fprintf(galog,"\n\n Simulation completed\n");

　　fprintf(galog,"\n Best member: \n");

　　fprintf (galog,"\n var(%d) = %3.3f",i,population[POPSIZE].gene[i]);

　　fprintf(galog,"\n\n Best fitness = %3.3f",population[POPSIZE].fitness);

　　fclose(galog);

　　printf("Success\n");

本文转自feisky博客园博客，原文链接：http://www.cnblogs.com/feisky/archive/2008/04/11/1586624.html，如需转载请自行联系原作者