1、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；且函数值和个体的适应值之间没有区别。该系统使用比率
2、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 implementa
3、tion 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 #include /* Change any of these parameters to match your needs */#define POPSIZE 50 /* population size */#define MAXGENS
4、 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 rfi
6、tness; /* relative fitness 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 ini
7、tialize(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 */*
8、within 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 */* 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);/*
10、initialize variables within the bounds */for (i = 0; i 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 populationi+1.fitness) if (populationi.fitness = best)best = population
11、i.fitness;best_mem = i;if (populationi+1.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 individu
12、al 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+)populationworst_mem.genei = populationPOPSIZE.genei;populationworst_mem.fitness = populationPOPSIZE.fitness;/*/* Selection function: Standard proportional selection for */* maximization problems incorporating elitist model - makes */* sure that the best member survives */*/
