Genetic Algo Assignment 2

#include <iostream>
#include <vector>
#include <math.h>
using namespace std;

struct Chromosome
{
    float fitness = 100000 ;
    vector <float> Genes ; // a0 , a1 , a2 , ...... , an   = Polynomial Degree + 1
};

struct Point
{
    float x ;
    float y ;
};

int const numIndividuals = 1000 ;
int const numGenerations = 10 ;
int numPoints ;
int numCases ;
int PolyDeg ;
float Total_Fitness ;

vector <Point> Points ;

vector <Chromosome> Generation ;
vector <Chromosome> nxtGeneration ;


void read()
{
    freopen("input.txt", "rt", stdin);
    cin >> numCases ;
    for (int c = 0 ; c<numCases ; c++)
   {
       cin >> numPoints >> PolyDeg ;
       Point Temp ;
       for (int j = 0 ; j<numPoints ; j++)
       {
           cin >> Temp.x >> Temp.y ;
           Points.push_back(Temp);
       }
   }

}



void Intial_Population()
{
    Generation.resize(numIndividuals);
     for (int i = 0 ; i<numIndividuals ; i++)
        {

            Generation[i].Genes.resize(PolyDeg+1) ;

            for ( int j = 0 ; j <= PolyDeg ; j++)
             {

               float r = -10 + static_cast <float> (rand()) /( static_cast <float> (RAND_MAX/(10-(-10)))); //Generate Random Float Number between -10 to 10
               Generation[i].Genes[j] = r ;
            }
        }
}




void CalcFitness ()
{
  float error = 0 ;
  float sum = 0 ;
  float sum2 = 0 ;
  Total_Fitness = 0 ;

  for ( int z = 0 ; z < numIndividuals ; z++)
  {
      for ( int i = 0 ; i < numPoints ; i++)
      {
          float yCalc = 0 ;
          float x = Points[i].x ;

          for ( int j = 0 ; j <= PolyDeg ; j++)
          {
              yCalc = (float)(yCalc + (Generation[z].Genes[j])* (pow(x,j))) ;


          }
          sum = (float)((yCalc) - Points[i].y) ;   // Sum = Ycalc - Yactual
          sum2 = (pow(sum,2)); //Sum2 = sum^2

      }
      float val = 1 / (float) numPoints;

      error = (val*sum2) ;  // Error = 1/n MSE

      Generation[z].fitness = error ;
      Total_Fitness += Generation[z].fitness ;
  }
}


int Roulette ()
{

    float r = static_cast <float> (rand()) / (static_cast <float> (RAND_MAX/Total_Fitness)); //Generate Random Number Between 0 and Total Case Fitness
    int sum = 0 ;
    for ( int i = 0 ; i < numIndividuals ; ++i)
    {

       sum += Generation[i].fitness;

       if ( sum > r ) // The First Chromosome to affect the fitness will be returned
       {
           return i ;
       }
    }
    return -1 ;
}


void Mutate (Chromosome & Chr)
{
    float Upper = 10.0 , Lower = -10.0 ,dUpper , dLower ;
    for (int i = 0; i <Chr.Genes.size(); ++i)
    {
        int m = rand() % 1000;
        if (m < 1)
          {
            dUpper = Upper - Chr.Genes[i] ;
            dLower = Chr.Genes[i] - Lower ;

            int r = rand() % 10;
            if (r < 5 )
            {
               float r2 = static_cast <float> (rand()) / (static_cast <float> (RAND_MAX/dUpper));
               Chr.Genes[i] += r2 ;
            }
            else
            {
               float r3 = static_cast <float> (rand()) / (static_cast <float> (RAND_MAX/dLower));
               Chr.Genes[i] -= r3 ;
            }
          }
    }

}


void CrossOver (int Cindex1 , int Cindex2)
{
    Chromosome Chr1 = Generation[Cindex1]; //Takes First Chromosome From the population
    Chromosome Chr2 = Generation[Cindex2]; //Takes Second Chromosome From the population
    int r =  rand()%10 ; //Generate Random Number from 0 to 10

    if ( r < 6 )
    {

    int r = rand()%PolyDeg; //Generate Random Number from 0 to Number of Items
    r++;
    for ( int i = 0 ; i < r ; i++)
        swap(Chr1.Genes[i],Chr2.Genes[i]);

    Mutate(Chr1); //Makes Mutation To First Chromosome
    Mutate(Chr2); //Makes Mutation To Second Chromosome
    }

    nxtGeneration.push_back(Chr1); //Store The Chromosome in The offspring
    nxtGeneration.push_back(Chr2); //Store The Chromosome in The offspring
}


void Reproduce()
 {

    while (nxtGeneration.size() < numIndividuals) //Until offspring size is equal to number of individuals
        {
        int chromosome_index1 = Roulette(); //choose random chromosome
        int chromosome_index2 = Roulette(); //choose random chromosome
        CrossOver(chromosome_index1, chromosome_index2); //Makes crossover between them

        }
}


int Get_Fit()
{
    int best_fitness = 1000000, index = -1;
    for (int i = 0; i < numIndividuals; ++i)
        {
        if (best_fitness > Generation[i].fitness)
        {
            best_fitness = Generation[i].fitness;
            index = i;
        }
    }
    return index;
}


void Print ()
{
    int BestFit = Get_Fit();
    float Result = Generation[BestFit].fitness;

    for ( int i = 0 ; i<= PolyDeg ; i++)
        cout << Generation[BestFit].Genes[i] << ", ";

    cout << endl << "It's Fitness : " << Result << endl << endl ;
}



void Compute ()
{

    read(); // First Read The Chromosome from the file

    for (int c = 0 ; c < numCases ; c++)
    {
        nxtGeneration.clear(); //To Empty the offspring

        Intial_Population(); // Generates The Initial population

        int Gen = 0 ;
        while ( Gen < numGenerations) //Continue Until I Reach Maximum Generation Number
        {
            if ( Gen != 0 )
            {
                Generation = nxtGeneration ; //Replace the old generation chromosomes with the offspring ones
                nxtGeneration.clear(); //And Empty the offspring
            }
            CalcFitness(); // Calculate fitness of each chromosome
            Gen++ ;
            if ( Gen < numGenerations)
            {
                Reproduce(); // Makes New Generation
            }


        }

        Print(); // Print This Case Output
    }
}


int main()
{


    Compute();


    return 0;
}