C + + realizes simple genetic algorithm _c language

This article illustrates a simple genetic algorithm for C + + implementation. Share to everyone for your reference. The implementation method is as follows:

Genetic algorithm GA #include <iostream> #include <cstdlib> #include <bitset> using namespace std; const int l=5;  Defines the length of the encoded int f (int x)//define the function f (x) {int result; result=x*x*x-60*x*x+900*x+100 return result;} int main (int Argc,char *argv[]) {int a (0), B (32);//define the domain scope of x defined by const int pop_size=8;//define population size//int L;//Specify encoded length const int ng=20; number int t=0; Current propagation of algebraic int p[pop_size]; Define population int Q[pop_size]; The Next Generation Srand (6553) defining the breeding population, i.e. the population; To define the seed of random number generation double sum; Fitness sum double avl_sum; Moderate average value double p_probability[pop_size];
Fitness probability double pp[pop_size]; Double Pro; Define the probability of randomly generated float pc=0.90; Define cross probability float pm=0.05; 
The probability of defining variation cout<< "initial population";
  for (int i=0;i<pop_size;i++)//Generate initial No. 0 generation population {p[i]=rand ()%31; cout<<p[i]<< "";
   } cout<<endl;
   cout<<endl; void Xover (int &,int &); Declaring the Cross function//when the stop criterion is not satisfied that the propagation algebra is not to the maximum algebra, continues to reproduce while (t<=ng) {cout<< "reproducing algebra: t=" <<t<<endl; sum=0.
0; for (int i=0;i<pop_size;i++) {q[i]=p[i]; cout<<q[i]<< "";
 } cout<<endl;
 for (int i=0;i<pop_size;i++)//Compute sum sum +=f (p[i]);
 Avl_sum=sum/pop_size;
 cout<< "sum=" <<sum<<endl; 
    cout<< "moderate average value =" <<avl_sum<<endl; for (int i=0;i<pop_size;i++)//Calculate the fitness probability {p_probability[i]=f (p[i])/sum; if (i==0) {pp[i]=p_probability[i]; cout
<< "pp" <<i<< "=" <<pp[i]<<endl;
    } else {pp[i]=p_probability[i]+pp[i-1];
      cout<< "pp" <<i<< "=" <<pp[i]<<endl;
    }//cout<< "P_probability" <<i<< "=" <<p_probability[i]<<endl; //select parent for (int i=0;i<pop_size;i++) {pro=rand ()%1000/1000.0; if (pro>=pp[0]&&pro<pp[ 
1]) p[i]=q[0];
     else if (pro>=pp[1]&&pro<pp[2]) p[i]=q[1];
     else if (pro>=pp[2]&&pro<pp[3]) p[i]=q[2];
    else if (pro>=pp[3]&&pro<pp[4])   P[I]=Q[3];
     else if (pro>=pp[4]&&pro<pp[5]) p[i]=q[4]; 
     else p[i]=q[5];
///cross operator int r=0; 
int z=0;
 for (int j=0;j<pop_size;j++) {pro=rand ()%1000/1000.0; if (pro<pc) {++z;
 if (z%2==0) xover (P[r],p[j]); 
else r=j; }//mutation operator for (int i=1;i<=pop_size;i++) for (int j=0;j<l;j++) {pro=rand ()%1000/1000.0;////////////() generate random number in "0,1" interval if (PRO&L      
T;PM) {bitset<l>v (p[i]);
V.flip (j);
P[i]=v.to_ulong ();
}} t++; cout<<endl;  
 Population breeding Generation} cout<< "final result:";
  for (int i (0); i<pop_size;i++)//algorithm end, output {cout<<p[i]<< "";
} cout<<endl;
return 0; }//Definition crossover operation void Xover (int &a,int &b) {int pos;////randomly generated hybrid point that is the first component to Exchange Pos=rand ()%5+1;
  OS component int j,k;
  J=pos;
K=pos;
Bitset<l>e (a); Bitset<l>f (b);            
The former POS components are exchanged for each other bitset<l>g;
bitset<l>h; 
   for (int i=0;i<pos;i++) {if (e[i]==1) g.set (i); for (int i=0;i<pos;i++) {iF (f[i]==1) h.set (i);
   for (j;j<l;j++) {if (f[j]==1) G.set (j);
   for (k;k<l;k++) {if (e[k]==1) H.set (k);
} a=g.to_ulong ();  
B=h.to_ulong ();

 }

I hope this article will help you with your C + + programming.