遗传算法C语言源代码(一元函数和二元函数).docx

C语言遗传算法代码
以下为遗传算法的源代码，计算一元代函数的代码和二元函数的代码以+++++++++++++++++++++++++++++++++++++为分割线分割开来，请自行选择适合的代码，使用时请略看完代码的注释，在需要更改的地方更改为自己需要的代码。
+++++++++++++++++++++++++++++++一元函数代码++++++++++++++++++++++++++++
#include <stdio.h>
#include<stdlib.h>
#include<time.h>
#include<math.h>
#define POPSIZE 1000
#define maximization 1
#define minimization 2
#define cmax 100
#define cmin 0
#define length1 20
#define chromlength length1 //染色体长度

//注意，你是求最大值还是求最小值
int functionmode=minimization;

//变量的上下限的修改开始
float min_x1=-2;//变量的下界
float max_x1=-1;//变量的上界
//变量的上下限的修改结束

int popsize; //种群大小
int maxgeneration; //最大世代数
double pc; //交叉率
double pm; //变异率
struct individual
{
char chrom[chromlength+1];
double value;
double fitness; //适应度
};
int generation; //世代数
int best_index;
int worst_index;
struct individual bestindividual; //最佳个体
struct individual worstindividual; //最差个体
struct individual currentbest;
struct individual population[POPSIZE];
//函数声明
void generateinitialpopulation();
void generatenextpopulation();
void evaluatepopulation();
long decodechromosome(char *,int,int);
void calculateobjectvalue();
void calculatefitnessvalue();
void findbestandworstindividual();
void performevolution();
void selectoperator();
void crossoveroperator();
void mutationoperator();
void input();
void outputtextreport();

void generateinitialpopulation( ) //种群初始化
{
int i,j;

for (i=0;i<popsize; i++)
{
for(j=0;j<chromlength;j++)
{
population[i].chrom[j]=(rand()%20<10)?'0':'1';
}
population[i].chrom[chromlength]='\0';
}
}
void generatenextpopulation() //生成下一代
{
selectoperator();
crossoveroperator();
mutationoperator();
}
void evaluatepopulation() //评价个体，求最佳个体
{
calculateobjectvalue();
calculatefitnessvalue();
findbestandworstindividual();
}
long decodechromosome(char *string ,int point,int length) //给染色体解码
{
int i;
long decimal=0;
char*pointer;
for(i=0,pointer=string+point;i<length;i++,pointer++)
if(*pointer-'0')

{decimal +=(long)pow(2,i);
}
return (decimal);

}
void calculateobjectvalue() //计算函数值
{
int i;
long temp1,temp2;
double x1;

for (i=0; i<popsize; i++)
{
temp1=decodechromosome(population[i].chrom,0,length1);

x1=(max_x1-min_x1)*temp1/(1024*1024-1)+min_x1;

//目标函数修改开始

population[i].value=(pow(x1,5)-3*x1-1)*(pow(x1,5)-3*x1-1);
//目标函数修改结束

}
}
void calculatefitnessvalue()//计算适应度
{
int i;
double temp;
for(i=0;i<popsize;i++)
{
if(functionmode==maximization)
{if((population[i].value+cmin)>0.0)
{temp=cmin+population[i].value;}
else
{temp=0.0;
}
}
else if (functionmode==minimization)
{
if(population[i].value<cmax)
{temp=cmax-population[i].value;}
else{ temp=0.0;}
}
population[i].fitness=temp;
}
}
void findbestandworstindividual( ) //求最佳个体和最差个体
{
int i;
double sum=0.0;

bestindividual=population[0];
worstindividual=population[0];

for (i=1;i<popsize; i++){
if (population[i].fitness>bestindividual.fitness){
bestindividual=population[i];
best_index=i;
}
else if (population[i].fitness<worstindividual.fitness)
{
worstindividual=population[i];
worst_index=i;

}
sum+=population[i].fitness;
}
if (generation==0){
currentbest=bestindividual;
}
else{
if(bestindividual.fitness>=currentbest.fitness){
currentbest=bestindividual;
}
}
}
void performevolution() //演示评价结果
{
if (bestindividual.fitness>currentbest.fitness){
currentbest=population[best_index];
}
else{
population[worst_index]=currentbest;
}
}
void selectoperator() //比例选择算法
{
int i,index;
double p,sum=0.0;
double cfitness[POPSIZE];

struct individual newpopulation[POPSIZE];
for(i=0;i<popsize;i++)
{sum+=population[i].fitness;}

for(i=0;i<popsize; i++){
cfitness[i]=population[i].fitness/sum;
}

for(i=1;i<popsize; i++){
cfitness[i]=cfitness[i-1]+cfitness[i];
}

for (i=0;i<popsize;i++)
{
p=rand()%1000/1000.0;
index=0;
while (p>cfitness[index])
{
index++;
}
newpopulation[i]=population[index];
}
for(i=0;i<popsize; i++){
population[i]=newpopulation[i];
}
}
void crossoveroperator() //交叉算法
{
int i,j;
int index[POPSIZE];
int point,temp;
double p;
char ch;

for (i=0;i<popsize;i++){
index[i]=i;
}
for (i=0;i<popsize;i++){
point=rand()%(popsize-i);
temp=index[i];
index[i]=index[point+i];
index[point+i]=temp;
}

for (i=0;i<popsize-1;i+=2){
p=rand()%1000/1000.0;
if (p<pc){
point=rand()%(chromlength-1)+1;
for (j=point; j<chromlength;j++){
ch=population[index[i]].chrom[j];
population[index[i]].chrom[j]=population[index[i+1]].chrom[j];
population[index[i+1]].chrom[j]=ch;
}
}
}

}
void mutationoperator() //变异操作
{
int i,j;
double p;

for (i=0;i<popsize;i++){
for(j=0;j<chromlength;j++){
p=rand()%1000/1000.0;
if (p<pm){
population[i].chrom[j]=(population[i].chrom[j]=='0')?'1':'0';
}
}
}
}
void input() //数据输入
{ //printf("初始化全局变量:\n");
//printf(" 种群大小(50-500)：");
//scanf("%d", &popsize);
popsize=500;
if((popsize%2) != 0)
{
//printf( " 种群大小已设置为偶数\n");
popsize++;};
//printf(" 最大世代数(100-300)：");
//scanf("%d", &maxgeneration);
maxgeneration=200;
//printf(" 交叉率(0.2-0.99)：");
//scanf("%f", &pc);
pc=0.95;
//printf(" 变异率(0.001-0.1)：");
//scanf("%f", &pm);
pm=0.03;
}
void outputtextreport()//数据输出
{
int i;
double sum;
double average;
sum=0.0;
for(i=0;i<popsize;i++)
{sum+=population[i].value;}
average=sum/popsize;

printf("当前世代=%d\n当前世代平均函数值=%f\n当前世代最优函数值=%f\n",generation,average,population[best_index].value);

}
void main() //主函数
{ int i;
long temp1,temp2;
double x1,x2;
generation=0;
input();
generateinitialpopulation();
evaluatepopulation();
while(generation<maxgeneration)
{
generation++;
generatenextpopulation();
evaluatepopulation();
performevolution();
outputtextreport();
}
printf("\n");
printf(" 统计结果: ");
printf("\n");
//printf("最大函数值等于：%f\n",currentbest.fitness);
printf("其染色体编码为：");
for (i=0;i<chromlength;i++)
{
printf("%c",currentbest.chrom[i]);

}
printf("\n");
temp1=decodechromosome(currentbest.chrom,0,length1);

x1=(max_x1-min_x1)*temp1/(1024*1024-1)+min_x1;

printf("x1=%lf\n",x1);

//这是需要修改的地方
printf("最优值等于：%f\n",(pow(x1,5)-3*x1-1)*(pow(x1,5)-3*x1-1));
}
+++++++++++++++++++++++++二元函数代码+++++++++++++++++++++++++++++++++++++++++
#include <stdio.h>
#include<stdlib.h>
#include<time.h>
#include<math.h>
#define POPSIZE 500
#define maximization 1
#define minimization 2
#define cmax 100
#define cmin 0
#define length1 20
#define length2 20
#define chromlength length1+length2 //染色体长度
//-----------求最大还是最小值
int functionmode=maximization;
//-----------
//-----------变量上下界
float min_x1=0;
float max_x1=3;
float min_x2=1;
float max_x2=5;
//-----------
int popsize; //种群大小
int maxgeneration; //最大世代数
double pc; //交叉率
double pm; //变异率
struct individual
{
char chrom[chromlength+1];
double value;
double fitness; //适应度
};
int generation; //世代数
int best_index;
int worst_index;
struct individual bestindividual; //最佳个体
struct individual worstindividual; //最差个体
struct individual currentbest;
struct individual population[POPSIZE];
//函数声明
void generateinitialpopulation();
void generatenextpopulation();
void evaluatepopulation();
long decodechromosome(char *,int,int);
void calculateobjectvalue();
void calculatefitnessvalue();
void findbestandworstindividual();
void performevolution();
void selectoperator();
void crossoveroperator();
void mutationoperator();
void input();
void outputtextreport();

void generateinitialpopulation( ) //种群初始化
{
int i,j;

for (i=0;i<popsize; i++)
{
for(j=0;j<chromlength;j++)
{
population[i].chrom[j]=(rand()%40<20)?'0':'1';
}
population[i].chrom[chromlength]='\0';
}
}
void generatenextpopulation() //生成下一代
{
selectoperator();
crossoveroperator();
mutationoperator();
}
void evaluatepopulation() //评价个体，求最佳个体
{
calculateobjectvalue();
calculatefitnessvalue();
findbestandworstindividual();
}
long decodechromosome(char *string ,int point,int length) //给染色体解码
{
int i;
long decimal=0;
char*pointer;
for(i=0,pointer=string+point;i<length;i++,pointer++)
if(*pointer-'0')

{decimal +=(long)pow(2,i);
}
return (decimal);

}
void calculateobjectvalue() //计算函数值
{
int i;
long temp1,temp2;
double x1,x2;

for (i=0; i<popsize; i++)
{
temp1=decodechromosome(population[i].chrom,0,length1);
temp2=decodechromosome(population[i].chrom,length1,length2);
x1=(max_x1-min_x1)*temp1/(1024*1024-1)+min_x1;
x2=(max_x2-min_x2)*temp2/(1024*1024-1)+min_x2;
//-----------函数
population[i].value=x1*x1+sin(x1*x2)-x2*x2;
//-----------
}
}
void calculatefitnessvalue()//计算适应度
{
int i;
double temp;
for(i=0;i<popsize;i++)
{
if(functionmode==maximization)
{if((population[i].value+cmin)>0.0)
{temp=cmin+population[i].value;}
else
{temp=0.0;
}
}
else if (functionmode==minimization)
{
if(population[i].value<cmax)
{temp=cmax-population[i].value;}
else{ temp=0.0;}
}
population[i].fitness=temp;
}
}
void findbestandworstindividual( ) //求最佳个体和最差个体
{
int i;
double sum=0.0;

bestindividual=population[0];
worstindividual=population[0];

for (i=1;i<popsize; i++){
if (population[i].fitness>bestindividual.fitness){
bestindividual=population[i];
best_index=i;
}
else if (population[i].fitness<worstindividual.fitness)
{
worstindividual=population[i];
worst_index=i;

}
sum+=population[i].fitness;
}
if (generation==0){
currentbest=bestindividual;
}
else{
if(bestindividual.fitness>=currentbest.fitness){
currentbest=bestindividual;
}
}
}
void performevolution() //演示评价结果
{
if (bestindividual.fitness>currentbest.fitness){
currentbest=population[best_index];
}
else{
population[worst_index]=currentbest;
}
}
void selectoperator() //比例选择算法
{
int i,index;
double p,sum=0.0;
double cfitness[POPSIZE];

struct individual newpopulation[POPSIZE];
for(i=0;i<popsize;i++)
{sum+=population[i].fitness;}

for(i=0;i<popsize; i++){
cfitness[i]=population[i].fitness/sum;
}

for(i=1;i<popsize; i++){
cfitness[i]=cfitness[i-1]+cfitness[i];
}

for (i=0;i<popsize;i++)
{
p=rand()%1000/1000.0;
index=0;
while (p>cfitness[index])
{
index++;
}
newpopulation[i]=population[index];
}
for(i=0;i<popsize; i++){
population[i]=newpopulation[i];
}
}
void crossoveroperator() //交叉算法
{
int i,j;
int index[POPSIZE];
int point,temp;
double p;
char ch;

for (i=0;i<popsize;i++){
index[i]=i;
}
for (i=0;i<popsize;i++){
point=rand()%(popsize-i);
temp=index[i];
index[i]=index[point+i];
index[point+i]=temp;
}

for (i=0;i<popsize-1;i+=2){
p=rand()%1000/1000.0;
if (p<pc){
point=rand()%(chromlength-1)+1;
for (j=point; j<chromlength;j++){
ch=population[index[i]].chrom[j];
population[index[i]].chrom[j]=population[index[i+1]].chrom[j];
population[index[i+1]].chrom[j]=ch;
}
}
}

}
void mutationoperator() //变异操作
{
int i,j;
double p;

for (i=0;i<popsize;i++){
for(j=0;j<chromlength;j++){
p=rand()%1000/1000.0;
if (p<pm){
population[i].chrom[j]=(population[i].chrom[j]=='0')?'1':'0';
}
}
}
}
void input() //数据输入
{ //printf("初始化全局变量:\n");
//printf(" 种群大小(50-500)：");
//scanf("%d", &popsize);
popsize=200;
if((popsize%2) != 0)
{
//printf( " 种群大小已设置为偶数\n");
popsize++;};
//printf(" 最大世代数(100-300)：");
//scanf("%d", &maxgeneration);
maxgeneration=200;
//printf(" 交叉率(0.2-0.99)：");
//scanf("%f", &pc);
pc=0.9;
//printf(" 变异率(0.001-0.1)：");
//scanf("%f", &pm);
pm=0.003;
}
void outputtextreport()//数据输出
{
int i;
double sum;
double average;
sum=0.0;
for(i=0;i<popsize;i++)
{sum+=population[i].value;}
average=sum/popsize;

printf("当前世代=%d\n当前世代平均函数值=%f\n当前世代最优函数值=%f\n",generation,average,population[best_index].value);

}
void main() //主函数
{ int i;
long temp1,temp2;
double x1,x2;
generation=0;
input();
generateinitialpopulation();
evaluatepopulation();
while(generation<maxgeneration)
{
generation++;
generatenextpopulation();
evaluatepopulation();
performevolution();
outputtextreport();
}
printf("\n");
printf(" 统计结果: ");
printf("\n");
//printf("最大函数值等于：%f\n",currentbest.fitness);
printf("其染色体编码为：");
for (i=0;i<chromlength;i++)
{
printf("%c",currentbest.chrom[i]);

}
printf("\n");
temp1=decodechromosome(currentbest.chrom,0,length1);
temp2=decodechromosome(currentbest.chrom,length1,length2);
x1=(max_x1-min_x1)*temp1/(1024*1024-1)+min_x1;
x2=(max_x2-min_x2)*temp2/(1024*1024-1)+min_x2;
printf("x=%lf,y=%lf\n",x1,x2);
//-----------修改函数
printf("最大值=%f\n",x1*x1+sin(x1*x2)-x2*x2);
//-----------
}