ga_max.cpp

VC++遗传算法求函数的最值,用的最简洁的方法!

/****************************************************************
本程序说明:这是一个非常简单的遗传算法源代码，
对一特定的应用修正此代码，你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。
********************************************************************/
  #include <stdio.h>
#include <stdlib.h>
#include <math.h>

#define POPSIZE 50               //种群个体数目 
#define MAXGENS 1000             //最大迭代数 
#define NVARS 3                  //函数的变量数
#define PXOVER 0.8               //交叉概率
#define PMUTATION 0.15           //变异概率
#define TRUE 1
#define FALSE 0
int generation;                  //当前的种群
int cur_best;                    //最优个体
FILE *galog;                     //输出文件
struct genotype //基因结构体定义
{
  double gene[NVARS];        //字符串变量
  double fitness;            //个体适应度
  double upper[NVARS];       //基因变量上界
  double lower[NVARS];       //下界
  double rfitness;           //相对适应度
  double cfitness;           //累积适应度
};
struct genotype population[POPSIZE+1];    //原种群定义数组
struct genotype newpopulation[POPSIZE+1]; //新种群,将替代旧种群
                                          
//函数申明
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);

//函数实现
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);
		  }
	//初始化变量
	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);
}
/***********************************************************/
/* 产生一个在变量范围内的随机数 */
/***********************************************************/
double randval(double low, double high)
{
	double val;
	val = ((double)(rand()%1000)/1000.0)*(high - low) + low;
	return(val);
}
/*************************************************************/
/* 定义要求极值的适应度函数  */
/* 这里的函数是:  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];
		  }
}


/***************************************************************/
//获得最佳个体的函数
/***************************************************************/

void keep_the_best()
{
	int mem;
	int i;
	cur_best = 0; //保存最佳个体的索引值
	for (mem = 0; mem < POPSIZE; mem++)
		  {
		  if (population[mem].fitness > population[POPSIZE].fitness)
				{
				cur_best = mem;
				population[POPSIZE].fitness = population[mem].fitness;
				}
		  }
	//如果找到最佳成则保存
	for (i = 0; i < NVARS; i++)
		  population[POPSIZE].gene[i] = population[cur_best].gene[i];
}
/****************************************************************/
/*                           */
/****************************************************************/
void elitist()
{
	int i;
	double best, worst;             //最好与最坏适应度值
	int best_mem, worst_mem;        //最好与最坏适应度值的索引 
	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 >= 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;
		} 
}
/**************************************************************/
/*标准选择                   */
/**************************************************************/
void select(void)
{
	int mem, i, j, k;
	double sum = 0;
	double p;
	for (mem = 0; mem < POPSIZE; mem++)
		  {
		  sum += population[mem].fitness;
		  }
	for (mem = 0; mem < POPSIZE; mem++)
		  {
		  population[mem].rfitness =  population[mem].fitness/sum;
		  }
	population[0].cfitness = population[0].rfitness;
	for (mem = 1; mem < POPSIZE; mem++)
		  {
		  population[mem].cfitness =  population[mem-1].cfitness +       
							  population[mem].rfitness;
		  }

	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];
				}
		  }

	for (i = 0; i < POPSIZE; i++)
		  population[i] = newpopulation[i];      
}
/***************************************************************/
//选择两个父代参与交叉,方式为单点交叉
/***************************************************************/
void crossover(void)
{
	int i, mem, one;
	int first  =  0; 
	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;
				}
		  }
}
/**************************************************************/
/*完成交叉任务 */
/**************************************************************/
void Xover(int one, int two)
{
	int i;
	int 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]);
	   }
}
/*************************************************************/
/*************************************************************/
void swap(double *x, double *y)
{
	double temp;
	temp = *x;
	*x = *y;
	*y = temp;
}
/**************************************************************/
/* 随机变异程序                           */
/**************************************************************/
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)
					  {
					  
					  lbound = population[i].lower[j];
					  hbound = population[i].upper[j];  
					  population[i].gene[j] = randval(lbound, hbound);
					  }
				}
}
/***************************************************************/
/* 数据输出      */
/***************************************************************/
void report(void)
{
	int i;
	double best_val;            /* 最适群体应度 */
	double avg;                 /* 平均群适应度 */
	double stddev;              /*标准误差 */
	double sum_square;          
	double square_sum;          
	double sum;                
	sum = 0.0;
	sum_square = 0.0;
	for (i = 0; i < POPSIZE; i++)
		  {
		  sum += population[i].fitness;
		  sum_square += population[i].fitness * population[i].fitness;
		  }
	avg = sum/(double)POPSIZE;
	square_sum = avg * avg * POPSIZE;
	stddev = sqrt((sum_square - square_sum)/(POPSIZE - 1));
	best_val = population[POPSIZE].fitness;
	fprintf(galog, "\n%5d, %6.3f, %6.3f, %6.3f \n\n", generation, 
                                      best_val, avg, stddev);
}
/**************************************************************/
//主函数
/**************************************************************/
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");
}