遗传算法C语言代码.docx

1、遗传算法C语言代码遗传算法C语言代码遗传算法C语言代码遗传算法C语言代码/ 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

2、，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 #incl

3、ude #include /* Change 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

4、*/ #define PMUTATION 0.15 /* 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*/ d

5、ouble geneNVARS; /* a string 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*/ ; str

6、uct genotype populationPOPSIZE+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

7、(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 bounds. It also initiali

8、zes (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 format of the inp

9、ut 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 */ /把输入文件的变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 =

11、randval(populationj.loweri, 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 tak

12、es a user defined function. */ /* 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 = populationm

13、em.genei; populationmem.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;

14、int i; cur_best = 0; /* stores the index of the best individual */ /保存最佳个体的索引 for (mem = 0; mem populationPOPSIZE.fitness) cur_best = mem; populationPOPSIZE.fitness = populationmem.fitness; /* once the best member in the population is found, copy the genes */ /一旦找到种群的最佳个体，就拷贝他的基因 for (i = 0; i NVARS

15、; i+) populationPOPSIZE.genei = populationcur_best.genei; /*/ /* Elitist function: The best member of the previous 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 wo

16、uld replace the worst */ /* member of the current population */ /*/ /搜寻杰出个体函数：找出最好和最坏的个体。/如果某代的最好个体比前一代的最好个体要坏，那么后者将会取代当前种群的最坏个体void elitist() int i; double best, worst; /* best and worst fitness values 最好和最坏个体的适应度值*/ int best_mem, worst_mem; /* indexes of the best and worst member 最好和最坏个体的索引*/ best

17、 = 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 (populationi.fitness = best) best = populat

18、ioni+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 with the */ /* best one from th

19、e previous generation */ /如果新种群中的最好个体比前一代的最好个体要强的话，那么就把新种群的最好个体拷贝出来。 /否则就用前一代的最好个体取代这次的最坏个体 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+) populat

20、ionworst_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 */ /*/ /选择函数：用于最大化合并杰出模型的标准比例

21、选择，保证最优秀的个体得以生存void select(void) int mem, j, i; double sum = 0; double p; /* find total fitness of the population */ /找出种群的适应度之和 for (mem = 0; mem POPSIZE; mem+) sum += populationmem.fitness; /* calculate relative fitness */ /计算相对适应度 for (mem = 0; mem POPSIZE; mem+) populationmem.rfitness = populati

22、onmem.fitness/sum; population0.cfitness = population0.rfitness; /* calculate cumulative fitness */ /计算累加适应度 for (mem = 1; mem POPSIZE; mem+) populationmem.cfitness = populationmem-1.cfitness + populationmem.rfitness; /* finally select survivors using cumulative fitness. */ /用累加适应度作出选择 for (i = 0; i

23、POPSIZE; i+) p = rand()%1000/1000.0; if (p population0.cfitness) newpopulationi = population0; else for (j = 0; j = populationj.cfitness & ppopulationj+1.cfitness) newpopulationi = populationj+1; /* once a new population is created, copy it back */ /当一个新种群建立的时候，将其拷贝回去 for (i = 0; i POPSIZE; i+) popu

24、lationi = newpopulationi; /*/ /* Crossover selection: selects two parents that take part in */ /* the crossover. Implements a single point crossover */ /*/ /杂交函数：选择两个个体来杂交，这里用单点杂交void crossover(void) int 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 PXOVER) +first; if (first % 2 = 0) Xover(one, mem); else one = mem; /*/ /* Crossover: performs crossover of the two selected parents. */ /*/ void Xover(int one, int two) int i; int point; /* crossover point */ /* sel