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/************************************************************************************
* Simple Genetic Algorithm *
* *
* Version 1.0 *
* *
* Programmed by Rui GUO 1/7,2006 *
*********************************************************************************A*/
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h>
/**********************************************************************************
* The definition of constant *
* *
********************************************************************************B*/
#define POPSIZE 30
#define PI 3.1415926 //pi的大小
/**********************************************************************************
* THE DEFINATION OF USER DATA *
* *
********************************************************************************C*/
#define Cmax 10 //最大评价
#define Cmin 0 //最小
#define LENGTH1 22 //染色体长度
int PopSize=30; //种群规模
int MaxGeneration=150; //最大迭代次数=150
double Pc=0.8; //交叉概率 交叉率=0.8
double Pm=0.05; //变异概率 变异率=0.05
/*要求用使用基本遗传算法，遗传算法参数：染色体长度=22*/
/**********************************************************************************
* THE DEFINATION OF DATA STRUCTURE *
* *
********************************************************************************D*/
struct individual{
char chrom[LENGTH1+1]; //染色体
double value; //函数值
double fitness; //适应度
};
/**********************************************************************************
* THE DEFINATION OF GLOBLE VARIABLES *
* *
********************************************************************************E*/
int generation; //代数
int best_index;//最好的位置
int worst_index;//最坏的位置
struct individual bestindividual;//最好个体
struct individual worstindividual;//最坏个体
struct individual currentbest;//当前最好
struct individual population[POPSIZE];//种群
/**********************************************************************************
* DECLARATION OF PROTOTYPE *
* *
********************************************************************************F*/
void GenerateInitialPopulation(void);
void GenerateNextPopulation(void);
void EvaluatePopulation(void);
double DecodeChromosome(char *,int,int);
void CalulateObjectValue(void);
void CalulateFitnessValue(void);
void FindBestAndWorstIndividual(void);
void PerformEvolution(void);
void SelectionOperator(void);
void Crossoverperator(void);
void MutationOperator(void);
void OutputTextReport(void);
/**********************************************************************************
* main progrom *
* Variable: None *
* Note: *
********************************************************************************01*/
void main(void){
char c;
printf("***************************************************************************\n");
printf("一元函数优化优化问题：MAX f(x)=x*sin(10*PI*x)+2.0\n");
printf(" 交叉概率： 0.8\n 变异概率： 0.05\n 种群规模： 30\n");
printf(" 染色体长度： 22\n 最大迭代次数： 150");
printf("\n***************************************************************************\n");
printf("按'N'退出，按其余键继续：");
scanf("%c",&c);
if(c=='N'||c=='n')
{ printf("程序退出\n");
exit;
}
else
{ generation=0;
GenerateInitialPopulation();//种群初始化
EvaluatePopulation();//评价函数
while(generation<MaxGeneration)
{
generation++;
GenerateNextPopulation();//生成下一代
EvaluatePopulation();//评价函数
PerformEvolution();//表现型
OutputTextReport();//输出
}
}
//OutputTextReport();//输出//////////////////////////////////////////////
}
/**********************************************************************************
* Function: generate the first population *
* Variable: None *
* Note: *
********************************************************************************02*/
void GenerateInitialPopulation(void){
int i,j,temp;
time_t t;
for(i=0;i<PopSize;i++){
for(j=0;j<LENGTH1;j++){
srand( time(&t) );
temp=rand()%9;//得到随机数
population[i].chrom[j]=(temp<5)?'0':'1';
// printf("%c",population[i].chrom[j]);//输出值，以便于函数调整
}
population[i].chrom[LENGTH1]='\0';
//printf("\n%d\n",population[i].chrom);
}
}
/**********************************************************************************
* Function: initial the next generation *
* Variable: None *
* Note: *
********************************************************************************03*/
void GenerateNextPopulation(void){
SelectionOperator();//选择运算
Crossoverperator();//交叉运算
MutationOperator();//变异运算
}
/**********************************************************************************
* Function: evalution population according to certain formula *
* Variable: None *
* Note: *
********************************************************************************04*/
void EvaluatePopulation(void){//评价函数
CalulateObjectValue();//计算目标函数值
CalulateFitnessValue();//计算适应度
FindBestAndWorstIndividual();//最好和最差变量
}
/**********************************************************************************
* Function: To decode a binary chromosome into adecimal integer *
* Variable: None *
* Note: 关键，有疑问？？？？？？？？？？？？ *
********************************************************************************05*/
double DecodeChromosome(char *string,int point,int length){
int i;
double decimal=0L;//二进制解码
char *pointer;
for(i=0,pointer=string+point;i<length;i++,pointer++){
decimal+=(*pointer-'0')<<(length-1-i);
// printf("(%d) ",decimal);
}
return(decimal);
}
/**********************************************************************************
* Function: To caclculate object value *
* Variable: None *
* Note: *
********************************************************************************06*/
void CalulateObjectValue(void){
int i;
double temp1;
double x1;
//Rosenbrock function
for(i=0;i<PopSize;i++){
temp1=DecodeChromosome(population[i].chrom,0,LENGTH1);//解码
//printf("(%d,",temp1);
x1=temp1/(1024.0*1024.0*4.0)+1; //计算表现型
//printf("_%f_ ",x1);
population[i].value=x1*sin(10*PI*x1)+2.0;//计算函数值
//printf("|%f| ",population[i].value);
}
}
/**********************************************************************************
* Function: To caclculate fitness value *
* Variable: None *
* Note: *
********************************************************************************07*/
void CalulateFitnessValue(void){
int i;
double temp;
for(i=0;i<PopSize;i++)
population[i].fitness=population[i].value;//直接将函数值作为适应度
}
/**********************************************************************************
* Function: To find out the best individual so farcurrent *
* generation *
* Variable: None *
* Note: *
********************************************************************************08*/
void FindBestAndWorstIndividual(void){
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;}
}
}
/**********************************************************************************
* Function: To perform evolution operation based on elitise model *
* Elitist modest is to replace the worst indivadual of *
* this generation by the current best one *
* Variable: None *
* Note: *
********************************************************************************09*/
void PerformEvolution(void){
if(bestindividual.fitness>currentbest.fitness){
currentbest=population[best_index];//最好
}
else {
population[worst_index]=currentbest;//最坏
}
}
/**********************************************************************************
* Function: To reproduce a chromosome by proportional selection *
* Variable: None *
* Note: *
********************************************************************************10*/
void SelectionOperator(void){
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];
}
}
/**************************************************************************************
* Function: Crossover two chromosome by means of one_point crossover *
Variable: None *
* Note: *
************************************************************************************11*/
void Crossoverperator(void){
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()%(LENGTH1-1)+1;
for(j=point;j<LENGTH1;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;
}
}
}
}
/**************************************************************************************
* Function: Mutation of a chromosome *
Variable: None *
* Note: *
************************************************************************************12*/
void MutationOperator(void){
int i,j;
double p;
for(i=0;i<PopSize;i++){
for(j=0;j<LENGTH1;j++){
p=rand()%1000/1000.0;
if(p<Pm){
population[i].chrom[j]=(population[i].chrom[j]=='0')?'1':'0';
}
}
}
}
/**************************************************************************************
* Function: Output the results of current population *
Variable: None *
* Note: *
************************************************************************************13*/
void OutputTextReport(void){
int i;
double sum;
double XBest;
double average;
sum=0.0;
for(i=0;i<PopSize;i++){
sum+=population[i].value;
}
average=sum/PopSize;
printf("\n***************************************************************************\n");
printf("迭代次数=%d 平均值是=%f 最佳个体是=%f",generation,average,currentbest.value);
printf("\n 编码为：");
for(i=0;i<LENGTH1;i++){
printf("%c",currentbest.chrom[i]);
}
XBest=DecodeChromosome(currentbest.chrom,0,LENGTH1)/(1024.0*1024.0*4.0)+1.0;
printf("\nx的值是：%f\n",XBest);
}
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