基本遗传算法的C++语言实现.doc

/********************************************************************/
/* 基于基本遗传算法的函数最优化 SGA.C */
/* A Function Optimizer using Simple Genetic Algorithm */
/* developed from the Pascal SGA code presented by David E.Goldberg */
/*************************/
#include
#include
/* 全局变量 */
struct individual /* 个体*/
{
unsigned *chrom; /* 染色体 */
double fitness; /* 个体适应度*/
double varible; /* 个体对应的变量值*/
int xsite; /* 交叉位置 */
int parent[2]; /* 父个体 */
int *utility; /* 特定数据指针变量 */
};
struct bestever /* 最佳个体*/
{
unsigned *chrom; /* 最佳个体染色体*/
double fitness; /* 最佳个体适应度 */
double varible; /* 最佳个体对应的变量值 */
int generation; /* 最佳个体生成代 */
};
struct individual *oldpop; /* 当前代种群 */
struct individual *newpop; /* 新一代种群 */
struct bestever bestfit; /* 最佳个体 */
double sumfitness; /* 种群中个体适应度累计 */
double max; /* 种群中个体最大适应度 */
double avg; /* 种群中个体平均适应度 */
double min; /* 种群中个体最小适应度 */
float pcross; /* 交叉概率 */
float pmutation; /* 变异概率 */
int popsize; /* 种群大小 */
int lchrom; /* 染色体长度*/
int chromsize; /* 存储一染色体所需字节数 */
int gen; /* 当前世代数 */
int maxgen; /* 最大世代数 */
int run; /* 当前运行次数 */
int maxruns; /* 总运行次数 */
int printstrings; /* 输出染色体编码的判断，0 -- 不输出, 1 -- 输出 */
int nmutation; /* 当前代变异发生次数 */
int ncross; /* 当前代交叉发生次数 */

/* 随机数发生器使用的静态变量 */
static double oldrand[55];
static int jrand;
static double rndx2;
static int rndcalcflag;
/* 输出文件指针 */
FILE *outfp ;
/* 函数定义 */
void advance_random();
int flip(float);rnd(int, int);
void randomize();
double randomnormaldeviate();
float randomperc(),rndreal(float,float);
void warmup_random(float);
void initialize(),initdata(),initpop();
void initreport(),generation(),initmalloc();
void freeall(),nomemory(char *),report();
void writepop(),writechrom(unsigned *);
void preselect();
void statistics(struct individual *);
void title(),repchar (FILE *,char *,int);
void skip(FILE *,int);
int select();
void objfunc(struct individual *);
int crossover (unsigned *, unsigned *, unsigned *, unsigned *);
void mutation(unsigned *);

void initialize() /* 遗传算法初始化 */
{
/* 键盘输入遗传算法参数 */
initdata();
/* 确定染色体的字节长度 */
chromsize = (lchrom/(8*sizeof(unsigned)));
if(lchrom%(8*sizeof(unsigned))) chromsize++;
/*分配给全局数据结构空间 */
initmalloc();
/* 初始化随机数发生器 */
randomize();
/* 初始化全局计数变量和一些数值*/
nmutation = 0;
ncross = 0;
bestfit.fitness = 0.0;
bestfit.generation = 0;
/* 初始化种群，并统计计算结果 */
initpop();
statistics(oldpop);
initreport();
}

void initdata() /* 遗传算法参数输入 */
{
char answer[2];
popsize=30;
if((popsize%2) != 0)
{
fprintf(outfp, "种群大小已设置为偶数\n");
popsize++;
};
lchrom=22;
printstrings=1;
maxgen=150;
pcross=0.8;
pmutation=0.005;
}

void initpop() /* 随机初始化种群 */
{
int j, j1, k, stop;
unsigned mask = 1;
for(j = 0; j < popsize; j++)
{
for(k = 0; k < chromsize; k++)
{
oldpop[j].chrom[k] = 0;
if(k == (chromsize-1))
stop = lchrom - (k*(8*sizeof(unsigned)));
else
stop =8*sizeof(unsigned);
for(j1 = 1; j1 <= stop; j1++)
{
oldpop[j].chrom[k] = oldpop[j].chrom[k]<<1;
if(flip(0.5))
oldpop[j].chrom[k] = oldpop[j].chrom[k]|mask;
}
}
oldpop[j].parent[0] = 0; /* 初始父个体信息 */
oldpop[j].parent[1] = 0;
oldpop[j].xsite = 0;
objfunc(&(oldpop[j])); /* 计算初始适应度*/
}
}

void initreport() /* 初始参数输出 */
{
void skip();
skip(outfp,1);
fprintf(outfp," 基本遗传算法参数\n");
fprintf(outfp," -------------------------------------------------\n");
fprintf(outfp," 种群大小(popsize) = %d\n",popsize);
fprintf(outfp," 染色体长度(lchrom) = %d\n",lchrom);
fprintf(outfp," 最大进化代数(maxgen) = %d\n",maxgen);
fprintf(outfp," 交叉概率(pcross) = %f\n",pcross);
fprintf(outfp," 变异概率(pmutation) = %f\n",pmutation);
fprintf(outfp," -------------------------------------------------\n");
skip(outfp,1);
fflush(outfp);
}

void generation()
{
int mate1, mate2, jcross, j = 0;
/* 每代运算前进行预选 */
preselect();
/* 选择, 交叉, 变异 */
do
{
/* 挑选交叉配对 */
mate1 = select();
mate2 = select();
/* 交叉和变异 */
jcross = crossover(oldpop[mate1].chrom, oldpop[mate2].chrom, newpop[j].chrom, newpop[j+1].chrom);

mutation(newpop[j].chrom);
mutation(newpop[j+1].chrom);
/* 解码, 计算适应度 */
objfunc(&(newpop[j]));
/*记录亲子关系和交叉位置 */
newpop[j].parent[0] = mate1+1;
newpop[j].xsite = jcross;
newpop[j].parent[1] = mate2+1;
objfunc(&(newpop[j+1]));
newpop[j+1].parent[0] = mate1+1;
newpop[j+1].xsite = jcross;
newpop[j+1].parent[1] = mate2+1;
j = j + 2;
}
while(j < (popsize-1));

}

void initmalloc() /*为全局数据变量分配空间 */
{
unsigned nbytes;
char *malloc();
int j;
/* 分配给当前代和新一代种群内存空间 */
nbytes popsize*sizeof(struct individual);
if((oldpop = (struct individual *) malloc(nbytes)) == NULL)
nomemory("oldpop");
if((newpop = (struct individual *) malloc(nbytes)) == NULL)
nomemory("newpop");
/* 分配给染色体内存空间 */
nbytes = chromsize*sizeof(unsigned);
for(j = 0; j < popsize; j++)
{
if((oldpop[j].chrom = (unsigned *) malloc(nbytes)) == NULL)
nomemory("oldpop chromosomes");
if((newpop[j].chrom = (unsigned *) malloc(nbytes)) == NULL)
nomemory("newpop chromosomes");
}
if((bestfit.chrom = (unsigned *) malloc(nbytes)) == NULL)
nomemory("bestfit chromosome");

}

void freeall() /* 释放内存空间 */
{
int i;
for(i = 0; i < popsize; i++)
{
free(oldpop[i].chrom);
free(newpop[i].chrom);
}
free(oldpop);
free(newpop);
free(bestfit.chrom);
}

void nomemory(string) /* 内存不足，退出*/
char *string;
{
fprintf(outfp,"malloc: out of memory making %s!!\n",string);
exit(-1);
}

void report() /* 输出种群统计结果 */
{
void repchar(), skip();
void writepop(), writestats();
repchar(outfp,"-",80);
skip(outfp,1);
if(printstrings == 1)
{
repchar(outfp," ",((80-17)/2));
fprintf(outfp,"模拟计算统计报告 \n");
fprintf(outfp, "世代数 %3d", gen);
repchar(outfp," ",(80-28));
fprintf(outfp, "世代数 %3d\n", (gen+1));
fprintf(outfp,"个体 染色体编码");
repchar(outfp," ",lchrom-5);
fprintf(outfp,"适应度 父个体 交叉位置 ");
fprintf(outfp,"染色体编码 ");
repchar(outfp," ",lchrom-5);
fprintf(outfp,"适应度\n");
repchar(outfp,"-",80);
skip(outfp,1);
writepop(outfp);
repchar(outfp,"-",80);
skip(outfp,1);
}
fprintf(outfp,"第 %d 代统计: \n",gen);
fprintf(outfp,"总交叉操作次数 = %d, 总变异操作数 = %d\n",ncross,nmutation);
fprintf(outfp," 最小适应度：%f 最大适应度：%f 平均适应度 %f\n", min,max,avg);
fprintf(outfp," 迄今发现最佳个体 => 所在代数： %d ", bestfit.generation);
fprintf(outfp," 适应度：%f 染色体：", bestfit.fitness);
writechrom((&bestfit)->chrom);
fprintf(outfp," 对应的变量值: %f", bestfit.varible);
skip(outfp,1);
repchar(outfp,"-",80);
skip(outfp,1);
}

void writepop()
{
struct individual *pind;
int j;
for(j=0; j pind = &(oldpop[j]);
writechrom(pind->chrom);
fprintf(outfp," %8f | ", pind->fitness);
/* 新一代个体 */
pind = &(newpop[j]);
fprintf(outfp,"(%2d,%2d) %2d ",
pind->parent[0], pind->parent[1], pind->xsite);
writechrom(pind->chrom);
fprintf(outfp," %8f\n", pind->fitness);
}
}

void writechrom(chrom) /* 输出染色体编码 */
unsigned *chrom;
{
int j, k, stop;
unsigned mask = 1, tmp;
for(k = 0; k < chromsize; k++)
{
tmp = chrom[k];
if(k == (chromsize-1))
stop = lchrom - (k*(8*sizeof(unsigned)));
else
stop =8*sizeof(unsigned);
for(j = 0; j < stop; j++)
{
if(tmp&mask)
fprintf(outfp,"1");
else
fprintf(outfp,"0");
tmp = tmp>>1;
}
}
}

void preselect()
{
int j;
sumfitness = 0;
for(j = 0; j < popsize; j++) sumfitness += oldpop[j].fitness;
}

int select() /* 轮盘赌选择*/
{
extern float randomperc();
float sum, pick;
int i;
pick = randomperc();
sum = 0;
if(sumfitness != 0)
{
for(i = 0; (sum < pick) && (i < popsize); i++)
sum += oldpop[i].fitness/sumfitness;
}
else
i = rnd(1,popsize);
return(i-1);
}

void statistics(pop) /* 计算种群统计数据 */
struct individual *pop;
{
int i, j;
sumfitness = 0.0;
min = pop[0].fitness;
max = pop[0].fitness;
/* 计算最大、最小和累计适应度 */
for(j = 0; j < popsize; j++)
{
sumfitness = sumfitness + pop[j].fitness;
if(pop[j].fitness > max) max = pop[j].fitness;
if(pop[j].fitness < min) min = pop[j].fitness;

/* new global best-fit individual */
if(pop[j].fitness > bestfit.fitness)
{
for(i = 0; i < chromsize; i++)
bestfit.chrom[i] = pop[j].chrom[i];
bestfit.fitness = pop[j].fitness;
bestfit.varible = pop[j].varible;
bestfit.generation = gen;
}
}
/* 计算平均适应度 */
avg = sumfitness/popsize;
}

void title()
{
printf("SGA Optimizer Jean.Timex\n");
}

void repchar (outfp,ch,repcount)
FILE *outfp;
char *ch;
int repcount;
{
int j;
for (j = 1; j <= repcount; j++) fprintf(outfp,"%s", ch);
}

void skip(outfp,skipcount)
FILE *outfp;
int skipcount;
{
int j;
for (j = 1; j <= skipcount; j++) fprintf(outfp,"\n");
}

void objfunc(critter) /* 计算适应度函数值 */
struct individual *critter;
{
unsigned mask=1;
unsigned bitpos;
unsigned tp;
double pow(), bitpow ;
int j, k, stop;
critter->varible = 0.0;
for(k = 0; k < chromsize; k++)
{
if(k == (chromsize-1))
stop = lchrom-(k*(8*sizeof(unsigned)));
else
stop =8*sizeof(unsigned);
tp = critter->chrom[k];
for(j = 0 j < stop; j++)
{
bitpos = j + (8*sizeof(unsigned))*k;
if((tp&mask) == 1)
{
bitpow = pow(2.0,(double) bitpos);
critter->varible = critter->varible + bitpow;
}
tp = tp>>1;
}
}
critter->varible =-1+critter->varible*3/(pow(2.0,(double)lchrom)-1);
critter->fitness =critter->varible*sin(critter->varible*10*atan(1)*4)+2.0;
}

void mutation(unsigned *child) /*变异操作*/
{
int j, k, stop;
unsigned mask, temp = 1;
for(k = 0; k < chromsize; k++)
{
mask = 0;
if(k == (chromsize-1))
stop = lchrom - (k*(8*sizeof(unsigned)));
else
stop = 8*sizeof(unsigned);
for(j = 0; j < stop; j++)
{
if(flip(pmutation))
{
mask = mask|(temp< nmutation++;
}
}
child[k] = child[k]^mask;
}
}

int crossover (unsigned *parent1, unsigned *parent2, unsigned *child1, unsigned *child2)
/* 由两个父个体交叉产生两个子个体 */
{
int j, jcross, k;
unsigned mask, temp;
if(flip(pcross))
{
jcross = rnd(1 ,(lchrom - 1));/* Cross between 1 and l-1 */
ncross++;
for(k = 1; k <= chromsize; k++)
{
if(jcross >= (k*(8*sizeof(unsigned))))
{
child1[k-1] = parent1[k-1];
child2[k-1] = parent2[k-1];
}
else if((jcross < (k*(8*sizeof(unsigned)))) && (jcross > ((k-1)*(8*sizeof(unsigned)))))
{
mask = 1;
for(j = 1; j <= (jcross-1-((k-1)*(8*sizeof(unsigned)))); j++)
{
temp = 1;
mask = mask<<1;
mask = mask|temp;
}
child1[k-1] = (parent1[k-1]&mask)|(parent2[k-1]&(~mask));
child2[k-1] = (parent1[k-1]&(~mask))|(parent2[k-1]&mask);
}
else
{
child1[k-1] = parent2[k-1];
child2[k-1] = parent1[k-1];
}
}
}
else
{
for(k = 0; k < chromsize; k++)
{
child1[k] = parent1[k];
child2[k] = parent2[k];
}
jcross = 0;
}
return(jcross);
}

void advance_random() /* 产生55个随机数 */
{
int j1;
double new_random;
for(j1 = 0; j1 < 24; j1++)
{
new_random = oldrand[j1] - oldrand[j1+31];
if(new_random < 0.0) new_random = new_random + 1.0;
oldrand[j1] = new_random;
}
for(j1 = 24; j1 < 55; j1++)
{
new_random = oldrand [j1] - oldrand [j1-24];
if(new_random < 0.0) new_random = new_random + 1.0;
oldrand[j1] = new_random;
}
}

int flip(float prob) /* 以一定概率产生0或1 */
{
float randomperc();
if(randomperc() <= prob)
return1);
else
return(0);
}

void randomize() /* 设定随机数种子并初始化随机数发生器 */
{
float randomseed;
int j1;
for(j1=0; j1<=54; j1++)
oldrand[j1] = 0.0;
jrand=0;
randomseed=0.5;
warmup_random(randomseed);
}

double randomnormaldeviate() /* 产生随机标准差 */
{
double sqrt(), log(), sin(), cos();
float randomperc();
double t, rndx1;
if(rndcalcflag)
{ rndx1 = sqrt(- 2.0*log((double) randomperc()));
t = 6.2831853072 * (double) randomperc();
rndx2 = rndx1 * sin(t);
rndcalcflag = 0;
r