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分享哪位大神能帮我写个程序，用遗传算法求解投资组合的最优比例。
怎么用遗传算法求解在收益最大时各个证券的比例，其中证券的价格p,投资总额B，证券的协方差，收益率都是基本数据由输入函数输进去。
我找到一个遗传算法的c语言程序，就是适应度函数部分不知道怎么用上面的公式改进去，哪位大神能帮帮我
找到的程序如下：
#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 10
#define length2 10
#define chromlength length1+length2 //染色体长度
int functionmode=maximization;
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()%10<5)?'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=4.096*temp1/1023.0-2.048;
x2=4.096*temp2/1023.0-2.048;
population[i].value=100*(x1*x1-x2)* (x1*x1-x2)+(1-x1)*(1-x1);
}
}
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);
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);
printf(" 交叉率(0.2-0.99)：");
scanf("%f", &pc);
printf(" 变异率(0.001-0.1)：");
scanf("%f", &pm);
}
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当前世代最高函数值=%f\n",generation,average,population[best_index].value);
}
void main() //主函数
{ int i;
printf("本程序为求函数y=100*(x1*x1-x2)*(x1*x2-x2)+(1-x1)*(1-x1)的最大值 \n其中-2.048<=x1,x2<=2.048\n");
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");
}
...全文
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sinat_29039951 2016-04-14
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赵4老师 2016-04-14
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再牛×的老师也无法代替学生自己领悟和上厕所！
百度也无法代替楼主自己把这个代码的适应度函数改一下！
sinat_29039951 2016-04-14
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我是想把这个代码的适应度函数改一下
