遗传算法源代码（适合毕设）

/*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。*/ /**************************************************************************/ /* This is a simple genetic algorithm implementation 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 <stdio.h> #include <stdlib.h> #include <math.h> /* Change any of these parameters to match your needs */ #define POPSIZE 50 /* population size */ #define MAXGENS 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 0 int generation; /* current generation no. */ int cur_best; /* best individual */ FILE *galog; /* an output file */ struct genotype /* genotype (GT), a member of the population */ { double gene[NVARS]; /* a string of variables */ double fitness; /* GT's fitness */ double upper[NVARS]; /* GT's variables upper bound */ double lower[NVARS]; /* GT's variables lower bound */ double rfitness; /* relative fitness */ double cfitness; /* cumulative fitness */ }; struct genotype population[POPSIZE+1]; /* population */ struct genotype newpopulation[POPSIZE+1]; /* new population; */ /* replaces the */ /* old generation */ /* Declaration of procedures used by this genetic algorithm */ 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); /***************************************************************/ /* Initialization function: Initializes the values of genes */ /* 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 */ /* 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); } /* initialize variables within the bounds */ for (i = 0; i < NVARS; i++) { fscanf(infile, "%lf",&lbound); fscanf(infile, "%lf",&ubound); for (j = 0; j < POPSIZE; 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); } /***********************************************************/ /* 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 changed, the code has to be recompiled. */ /* The current function is: x[1]^2-x[1]*x[2]+x[3] */ /*************************************************************/ void evaluate(void) { int mem; int i; double x[NVARS+1]; for (mem = 0; mem < POPSIZE; mem++) { for (i = 0; i < NVARS; i++) x[i+1] = population[mem].gene[i]; population[mem].fitness = (x[1]*x[1]) - (x[1]*x[2]) + x[3]; } } /***************************************************************/ /* 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 */ /***************************************************************/ void keep_the_best() { int mem; int i; cur_best = 0; /* stores the index of the best individual */ for (mem = 0; mem < POPSIZE; mem++) { if (population[mem].fitness > population[POPSIZE].fitness) { cur_best = mem; population[POPSIZE].fitness = population[mem].fitness; } } /* once the best member in the population is found, copy the genes */ for (i = 0; i < NVARS; i++) population[POPSIZE].gene[i] = population[cur_best].gene[i]; } /****************************************************************/ /* 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 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; /* indexes of the best and worst member */ best = population[0].fitness; worst = population[0].fitness; for (i = 0; i < POPSIZE - 1; ++i) { 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 individual from the new population is better than */ /* the best individual from the previous population, then */ /* copy the best fr