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1、精选优质文档-倾情为你奉上/ GA.cpp : Defines the entry point for the console application./*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
2、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 #include #include /* Change
3、any of these parameters to match your needs */ /请根据你的需要来修改以下参数#define POPSIZE 50 /* population size 种群大小*/ #define MAXGENS 1000 /* max. number of generations 最大基因个数*/ const int NVARS = 3; /* no. of problem variables 问题变量的个数*/ #define PXOVER 0.8 /* probability of crossover 杂交概率*/ #define PMUTATION 0.
4、15 /* probability of mutation 变异概率*/ #define TRUE 1 #define FALSE 0 int generation; /* current generation no. 当前基因个数*/ int cur_best; /* best individual 最优个体*/ FILE *galog; /* an output file 输出文件指针*/ struct genotype /* genotype (GT), a member of the population 种群的一个基因的结构体类型*/ double geneNVARS; /* a s
5、tring of variables 变量*/ double fitness; /* GTs fitness 基因的适应度*/ double upperNVARS; /* GTs variables upper bound 基因变量的上界*/ double lowerNVARS; /* GTs variables lower bound 基因变量的下界*/ double rfitness; /* relative fitness 比较适应度*/ double cfitness; /* cumulative fitness 积累适应度*/ ; struct genotype population
6、POPSIZE+1; /* population 种群*/ struct genotype newpopulationPOPSIZE+1; /* new population; 新种群*/ /* replaces the old generation */ /取代旧的基因/* Declaration of procedures used by this genetic algorithm */ /以下是一些函数声明void initialize(void); double randval(double, double); void evaluate(void); void keep_the_b
7、est(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 bounds. It also initializes (to zero) */ /* all
8、 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 format of the input file gadata.txt is *
9、/ /* 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 the bounds */ /把输入文件的变
10、量界限输入到基因结构体中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.lowe
11、ri, populationj.upperi); fclose(infile); /*/ /* Random value generator: Generates a value within bounds */ /*/ /随机数产生函数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 functi
12、on. */ /* Each time this is changed, the code has to be recompiled. */ /* The current function is: x12-x1*x2+x3 */ /*/ /评价函数，可以由用户自定义，该函数取得每个基因的适应度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.
13、fitness = (x1*x1) - (x1*x2) + x3; /*/ /* Keep_the_best function: This function 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 */ /*/ /保存每次遗传后的最佳基因void keep_the_best() int mem; int i; cur_best = 0; /* stores the index of the best individual
