遗传算法的C语言实现

遗传算法的C语言实现

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 0int 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 from the new population; else replace the *//* worst individual from the current population with the *//* best one from the previous generation */if (best >= population[POPSIZE].fitness) { for (i = 0; i < NVARS; i++) population[POPSIZE].gene[i] = population[best_mem].gene[i]; population[POPSIZE].fitness = population[best_mem].fitness; }else { for (i = 0; i < NVARS; i++) population[worst_mem].gene[i] = population[POPSIZE].gene[i]; population[worst_mem].fitness = population[POPSIZE].fitness; } }/**************************************************************//* Selection function: Standard proportional selection for *//* maximization problems incorporating elitist model - makes *//* sure that the best member survives *//**************************************************************/void select(void){int mem, i, j, k;double sum = 0;double p;/* find total fitness of the population */for (mem = 0; mem < POPSIZE; mem++) { sum += population[mem].fitness; }/* calculate relative fitness */for (mem = 0; mem < POPSIZE; mem++) { population[mem].rfitness = population[mem].fitness/sum; }population[0].cfitness = population[0].rfitness;/* calculate cumulative fitness */for (mem = 1; mem < POPSIZE; mem++) { population[mem].cfitness = population[mem-1].cfitness + population[mem].rfitness; }/* finally select survivors using cumulative fitness. */for (i = 0; i < POPSIZE; i++) { p = rand()%1000/1000.0; if (p < population[0].cfitness) newpopulation[i] = population[0]; else { for (j = 0; j < POPSIZE;j++) if (p >= population[j].cfitness && p<population[j+1].cfitness) newpopulation[i] = population[j+1]; } }/* once a new population is created, copy it back */for (i = 0; i < POPSIZE; i++) population[i] = newpopulation[i]; }/***************************************************************//* Crossover selection: selects two parents that take part in *//* the crossover. Implements a single point crossover *//***************************************************************/void crossover(void){int i, 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 *//* select crossover point */if(NVARS > 1) { if(NVARS == 2) point = 1; else point = (rand() % (NVARS - 1)) + 1; for (i = 0; i < point; i++) swap(&population[one].gene[i], &population[two].gene[i]); }}/*************************************************************//* Swap: A swap procedure that helps in swapping 2 variables *//*************************************************************/void swap(double *x, double *y){double temp;temp = *x;*x = *y;*y = temp;}/**************************************************************//* Mutation: Random uniform mutation. A variable selected for *//* mutation is replaced by a random value between lower and *//* upper bounds of this variable *//**************************************************************/void mutate(void){int i, j;double lbound, hbound;double x;for (i = 0; i < POPSIZE; i++) for (j = 0; j < NVARS; j++) { x = rand()%1000/1000.0; if (x < PMUTATION) { /* find the bounds on the variable to be mutated */ lbound = population[i].lower[j]; hbound = population[i].upper[j]; population[i].gene[j] = randval(lbound, hbound); } }}/***************************************************************//* Report function: Reports progress of the simulation. Data *//* dumped into the output file are separated by commas *//***************************************************************/。。。。。代码太多 你到下面呢个网站看看吧void main(void){int i;if ((galog = fopen("galog.txt","w"))==NULL) { exit(1); }generation = 0;fprintf(galog, "\n generation best average standard \n");fprintf(galog, " number value fitness deviation \n");initialize();evaluate();keep_the_best();while(generation<MAXGENS) { generation++; select(); crossover(); mutate(); report(); evaluate(); elitist(); }fprintf(galog,"\n\n Simulation completed\n");fprintf(galog,"\n Best member: \n");for (i = 0; i < NVARS; i++) { 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");}