C + + realizes simple genetic algorithm

This paper illustrates a simple genetic algorithm for C + + implementation. Share to everyone for your reference. The implementation methods are as follows:

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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; NT 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<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; }//define crossover operation void Xover (int &a,int &b) {int pos;//randomly generated hybridization point namely the first several components are exchanged for each other pos=rand ()%5+1; in n components, randomly determine the POS 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.