?? ga_max.cpp
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/****************************************************************
本程序說明:這是一個非常簡單的遺傳算法源代碼，
對一特定的應用修正此代碼，你只需改變常數的定義并且定義"評價函數"即可。注意代碼 的設計是求最大值，其中的目標函數只能取正值；且函數值和個體的適應值之間沒有區別。該系統使用比率選擇、精華模型、單點雜交和均勻變異。如果用 Gaussian變異替換均勻變異，可能得到更好的效果。代碼沒有任何圖形，甚至也沒有屏幕輸出，主要是保證在平臺之間的高可移植性。讀者可以從ftp.uncc.edu, 目錄 coe/evol中的文件prog.c中獲得。要求輸入的文件應該命名為'gadata.txt'；系統產生的輸出文件為'galog.txt'。輸入的 文件由幾行組成：數目對應于變量數。且每一行提供次序--對應于變量的上下界。如第一行為第一個變量提供上下界，第二行為第二個變量提供上下界，等等。
********************************************************************/
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define POPSIZE 50 //種群個體數目
#define MAXGENS 1000 //最大迭代數
#define NVARS 3 //函數的變量數
#define PXOVER 0.8 //交叉概率
#define PMUTATION 0.15 //變異概率
#define TRUE 1
#define FALSE 0
int generation; //當前的種群
int cur_best; //最優個體
FILE *galog; //輸出文件
struct genotype //基因結構體定義
{
double gene[NVARS]; //字符串變量
double fitness; //個體適應度
double upper[NVARS]; //基因變量上界
double lower[NVARS]; //下界
double rfitness; //相對適應度
double cfitness; //累積適應度
};
struct genotype population[POPSIZE+1]; //原種群定義數組
struct genotype newpopulation[POPSIZE+1]; //新種群,將替代舊種群
//函數申明
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);
//函數實現
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);
}
//初始化變量
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);
}
/***********************************************************/
/* 產生一個在變量范圍內的隨機數 */
/***********************************************************/
double randval(double low, double high)
{
double val;
val = ((double)(rand()%1000)/1000.0)*(high - low) + low;
return(val);
}
/*************************************************************/
/* 定義要求極值的適應度函數 */
/* 這里的函數是: 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];
}
}
/***************************************************************/
//獲得最佳個體的函數
/***************************************************************/
void keep_the_best()
{
int mem;
int i;
cur_best = 0; //保存最佳個體的索引值
for (mem = 0; mem < POPSIZE; mem++)
{
if (population[mem].fitness > population[POPSIZE].fitness)
{
cur_best = mem;
population[POPSIZE].fitness = population[mem].fitness;
}
}
//如果找到最佳成則保存
for (i = 0; i < NVARS; i++)
population[POPSIZE].gene[i] = population[cur_best].gene[i];
}
/****************************************************************/
/* */
/****************************************************************/
void elitist()
{
int i;
double best, worst; //最好與最壞適應度值
int best_mem, worst_mem; //最好與最壞適應度值的索引
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;
}
}
/**************************************************************/
/*標準選擇 */
/**************************************************************/
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];
}
/***************************************************************/
//選擇兩個父代參與交叉,方式為單點交叉
/***************************************************************/
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;
}
}
}
/**************************************************************/
/*完成交叉任務 */
/**************************************************************/
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;
}
/**************************************************************/
/* 隨機變異程序 */
/**************************************************************/
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);
}
}
}
/***************************************************************/
/* 數據輸出 */
/***************************************************************/
void report(void)
{
int i;
double best_val; /* 最適群體應度 */
double avg; /* 平均群適應度 */
double stddev; /*標準誤差 */
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);
}
/**************************************************************/
//主函數
/**************************************************************/
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");
}
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