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文章由算法源码吧(www.sfcode.cn)收集
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。
#include
#include
#include
#define POPSIZE 50
#define MAXGENS 1000
#define NVARS 3
#define PXOVER 0.8
#define PMUTATION 0.15
#define TRUE 1
#define FALSE 0
int generation;
int cur_best;
FILE *galog;
struct genotype
{
double gene[NVARS];
double fitness;
double upper[NVARS];
double lower[NVARS];
double rfitness;
double cfitness;
};
struct genotype population[POPSIZE+1];
struct genotype newpopulation[POPSIZE+1];
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);
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);
}
for (i = 0; i
{
fscanf(infile, "%lf",&lbound);
fscanf(infile, "%lf",&ubound);
for (j = 0; 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);
}
double randval(double low, double high)
{
double val;
val = ((double)(rand()%1000)/1000.0)*(high - low) + low;
return(val);
}
void evaluate(void)
{
int mem;
int i;
double x[NVARS+1];
for (mem = 0; mem
{
for (i = 0; i
x[i+1] = population[mem].gene[i];
population[mem].fitness = (x[1]*x[1]) - (x[1]*x[2]) + x[3];
}
}
void keep_the_best()
{
int mem;
int i;
cur_best = 0;
for (mem = 0; mem
{
if (population[mem].fitness >population[POPSIZE].fitness)
{
cur_best = mem;
population[POPSIZE].fitness = population[mem].fitness;
}
}
for (i = 0; i
population[POPSIZE].gene[i] = population[cur_best].gene[i];
}
void elitist()
{
int i;
double best, worst;
int best_mem, worst_mem;
best = population[0].fitness;
worst = population[0].fitness;
for (i = 0; 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
population[POPSIZE].gene[i] = population[best_mem].gene[i];
population[POPSIZE].fitness = population[best_mem].fitness;
}
else
{
for (i = 0; i
population[worst_mem].gene[i] = population[POPSIZE].gene[i];
population[worst_mem].fitness = population[POPSIZE].fitness;
}
}
void select(void)
{
int mem, i, j, k;
double sum = 0;
double p;
for (mem = 0; mem
{
sum += population[mem].fitness;
}
for (mem = 0; mem
{
population[mem].rfitness = population[mem].fitness/sum;
}
population[0].cfitness = population[0].rfitness;
for (mem = 1; mem
{
population[mem].cfitness = population[mem-1].cfitness +
population[mem].rfitness;
}
for (i = 0; i
{
p = rand()%1000/1000.0;
if (p
newpopulation[i] = population[0];
else
{
for (j = 0; j
if (p >= population[j].cfitness &&
p
newpopulation[i] = population[j+1];
}
}
for (i = 0; i
population[i] = newpopulation[i];
}
void crossover(void)
{
int i, mem, one;
int first = 0;
double x;
for (mem = 0; mem
{
x = rand()%1000/1000.0;
if (x
{
++first;
if (first % 2 == 0)
Xover(one, mem);
else
one = mem;
}
}
}
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
swap(&population[one].gene[i], &population[two].gene[i]);
}
}
void swap(double *x, double *y)
{
double temp;
temp = *x;
*x = *y;
*y = temp;
}
void mutate(void)
{
int i, j;
double lbound, hbound;
double x;
for (i = 0; i
for (j = 0; j
{
x = rand()%1000/1000.0;
if (x
{
lbound = population[i].lower[j];
hbound = population[i].upper[j];
population[i].gene[j] = randval(lbound, hbound);
}
}
}
void report(void)
{
int i;
double best_val;
double avg;
double stddev;
double sum_square;
double square_sum;
double sum;
sum = 0.0;
sum_square = 0.0;
for (i = 0; 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);
}
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
{
generation++;
select();
crossover();
mutate();
report();
evaluate();
elitist();
}
fprintf(galog,"\n\n Simulation completed\n");
fprintf(galog,"\n Best member: \n");
for (i = 0; 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");
本文链接网址
本文转自
http://www.sfcode.cn/soft/00538835.htm
文章由算法源码吧(www.sfcode.cn)收集
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。
#include
#include
#include
#define POPSIZE 50
#define MAXGENS 1000
#define NVARS 3
#define PXOVER 0.8
#define PMUTATION 0.15
#define TRUE 1
#define FALSE 0
int generation;
int cur_best;
FILE *galog;
struct genotype
{
double gene[NVARS];
double fitness;
double upper[NVARS];
double lower[NVARS];
double rfitness;
double cfitness;
};
struct genotype population[POPSIZE+1];
struct genotype newpopulation[POPSIZE+1];
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);
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);
}
for (i = 0; i
{
fscanf(infile, "%lf",&lbound);
fscanf(infile, "%lf",&ubound);
for (j = 0; 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);
}
double randval(double low, double high)
{
double val;
val = ((double)(rand()%1000)/1000.0)*(high - low) + low;
return(val);
}
void evaluate(void)
{
int mem;
int i;
double x[NVARS+1];
for (mem = 0; mem
{
for (i = 0; i
x[i+1] = population[mem].gene[i];
population[mem].fitness = (x[1]*x[1]) - (x[1]*x[2]) + x[3];
}
}
void keep_the_best()
{
int mem;
int i;
cur_best = 0;
for (mem = 0; mem
{
if (population[mem].fitness >population[POPSIZE].fitness)
{
cur_best = mem;
population[POPSIZE].fitness = population[mem].fitness;
}
}
for (i = 0; i
population[POPSIZE].gene[i] = population[cur_best].gene[i];
}
void elitist()
{
int i;
double best, worst;
int best_mem, worst_mem;
best = population[0].fitness;
worst = population[0].fitness;
for (i = 0; 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
population[POPSIZE].gene[i] = population[best_mem].gene[i];
population[POPSIZE].fitness = population[best_mem].fitness;
}
else
{
for (i = 0; i
population[worst_mem].gene[i] = population[POPSIZE].gene[i];
population[worst_mem].fitness = population[POPSIZE].fitness;
}
}
void select(void)
{
int mem, i, j, k;
double sum = 0;
double p;
for (mem = 0; mem
{
sum += population[mem].fitness;
}
for (mem = 0; mem
{
population[mem].rfitness = population[mem].fitness/sum;
}
population[0].cfitness = population[0].rfitness;
for (mem = 1; mem
{
population[mem].cfitness = population[mem-1].cfitness +
population[mem].rfitness;
}
for (i = 0; i
{
p = rand()%1000/1000.0;
if (p
newpopulation[i] = population[0];
else
{
for (j = 0; j
if (p >= population[j].cfitness &&
p
newpopulation[i] = population[j+1];
}
}
for (i = 0; i
population[i] = newpopulation[i];
}
void crossover(void)
{
int i, mem, one;
int first = 0;
double x;
for (mem = 0; mem
{
x = rand()%1000/1000.0;
if (x
{
++first;
if (first % 2 == 0)
Xover(one, mem);
else
one = mem;
}
}
}
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
swap(&population[one].gene[i], &population[two].gene[i]);
}
}
void swap(double *x, double *y)
{
double temp;
temp = *x;
*x = *y;
*y = temp;
}
void mutate(void)
{
int i, j;
double lbound, hbound;
double x;
for (i = 0; i
for (j = 0; j
{
x = rand()%1000/1000.0;
if (x
{
lbound = population[i].lower[j];
hbound = population[i].upper[j];
population[i].gene[j] = randval(lbound, hbound);
}
}
}
void report(void)
{
int i;
double best_val;
double avg;
double stddev;
double sum_square;
double square_sum;
double sum;
sum = 0.0;
sum_square = 0.0;
for (i = 0; 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);
}
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
{
generation++;
select();
crossover();
mutate();
report();
evaluate();
elitist();
}
fprintf(galog,"\n\n Simulation completed\n");
fprintf(galog,"\n Best member: \n");
for (i = 0; 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");
本文链接网址
本文转自
http://www.sfcode.cn/soft/00538835.htm
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