UPDATE
After taking @glampert post into consideration and reading more about what he suggested, this is an updated version of the above code.
GeneticAlgorithm.h
#include <vector>
#include <random>
//the variables in the equation, which represnts the (a,b,c,d)
const int VARIABLES = 4;
//the size of chromosoms population
const int CHROMO_SIZE = 10000;
class GA
{
public:
struct Equation
{
int equation[VARIABLES];
float evaluation;
float fit;
float probability;
};
public:
GA();
//initialize the chromosoms
void initChromos(std::vector<Equation> &original, std::vector<Equation> &temp);
//calculate the fitness of chromosoms
void calcFitness(std::vector<Equation> &orginalCh);
//select the fit ones
void selectFitness(std::vector<Equation> &orginalCh);
//copy from parent one to parent two
void copyFromP1toP2(std::vector<Equation> &parent1,
std::vector<Equation> &parent2, int size);
//mutate the chromosoms and select two chromosoms to mate
void mutate(std::vector<Equation> &parent2, int i);
void mate(std::vector<Equation> &parent1, std::vector<Equation> &parent2);
//sort chromosoms by fitness
bool static sortFitness(Equation x, Equation y);
void sortByFitness(std::vector<Equation> &orginalCh);
//swap parent one and parent two for the next generation
void swap(std::vector<Equation> *&parent1, std::vector<Equation> *&parent2);
//print the best fit of every generation
void print(std::vector<Equation> &originalCh);
//generate random number
int randGenerate(int start, int end);
public:
std::default_random_engine dRandom;
private:
float total;
};
Implementation.cpp
#include<iostream>
#include <vector>
#include <algorithm>
#include<random>
#include "GeneticAlgorithm.h"
GA::GA(): total(0),dRandom()
{
}
void GA::initChromos(std::vector<Equation> &original, std::vector<Equation> &temp)
{
for(int i = 0; i < CHROMO_SIZE; i++)
{
GA::Equation e;
for(int j = 0; j < VARIABLES; j++)
{
int randNum = randGenerate(0,40);
e.equation[j]= randNum;
e.fit = 0;
}
original.push_back(e);
}
temp.resize(CHROMO_SIZE);
}
void GA::calcFitness(std::vector<Equation> &orginalCh)
{
for(int i = 0; i < CHROMO_SIZE; i++)
{
int j = 0;
orginalCh[i].evaluation = (float)abs((orginalCh[i].equation[j] + 2 * orginalCh[i].equation[ j + 1 ]
+ 3 * orginalCh[i].equation[ j + 2 ] + 4 * orginalCh[i].equation[ j + 3 ]) - 4 );
}
}
void GA::selectFitness(std::vector<Equation> &orginalCh)
{
for(int i = 0; i < CHROMO_SIZE; i++)
{
orginalCh[i].fit = 1 / ( 1 + orginalCh[i].evaluation);
total += orginalCh[i].fit;
}
float pro = 0;
for(int i = 0; i < CHROMO_SIZE; i++)
{
pro += orginalCh[i].fit / total;
orginalCh[i].probability = pro;
}
}
void GA::copyFromP1toP2(std::vector<Equation> &p1, std::vector<Equation> &p2, int s)
{
for(int i = 0; i < s; i++)
{
for(int j = 0; j < VARIABLES; j++)
{
p2[i].equation[j] = p1[i].equation[j];
}
p2[i].fit = p1[i].fit;
}
}
void GA::mutate(std::vector<Equation> &parent2, int i)
{
int index1 = randGenerate(0, VARIABLES - 1);
int index2 = randGenerate(0, VARIABLES - 1);
int j = randGenerate(0, CHROMO_SIZE - 1);
parent2[i].equation[index1] = parent2[j].equation[index2];
}
void GA::mate(std::vector<Equation> &parent1, std::vector<Equation> &parent2)
{
int sub, p1, p2, p3, tSsize = VARIABLES;
copyFromP1toP2(parent1, parent2, CHROMO_SIZE);
for(int i = 0; i < CHROMO_SIZE; i++)
{
p1 = randGenerate(0, CHROMO_SIZE - 1);
p2 = randGenerate(0, CHROMO_SIZE - 1);;
p3 = randGenerate(0, CHROMO_SIZE - 1);
sub = randGenerate(0, VARIABLES - 1);
for(int j=sub; j<VARIABLES; j++)
{
parent2[p2].equation[j] = abs(parent1[p1].equation[j] - parent1[p3].equation[j]);
}
if(parent2[i].evaluation > 0.5f)
mutate(parent2, i);
}
}
bool GA::sortFitness(Equation x, Equation y)
{
return(x.fit > y.fit);
}
void GA::sortByFitness(std::vector<Equation> &orginalCh)
{
std::sort(orginalCh.begin(),orginalCh.end(),&GA::sortFitness);
}
void GA::swap(std::vector<GA::Equation> *&parent1, std::vector<GA::Equation> *&parent2)
{
std::vector<GA::Equation> *temp = parent1;
parent1 = parent2;
parent2 = temp;
}
void GA::print(std::vector<Equation> &originalCh)
{
for(int i=0; i<VARIABLES; i++)
{
std::cout<< originalCh[0].equation[i] << ", ";
}
std::cout << " fitness = " << originalCh[0].fit;
std::cout<< std::endl;
}
int GA::randGenerate(int start, int end)
{
std::uniform_int_distribution<int> genenNum(start, end - 1);
return genenNum(dRandom);
}
int main()
{
GA ga;
std::vector<GA::Equation> chromO, chromT;
std::vector<GA::Equation> *originalCh, *bufferCh;
ga.initChromos(chromO, chromT);
originalCh = &chromO;
bufferCh = &chromT;
for(int i=0; i<2000; i++)
{
ga.calcFitness(*originalCh);
ga.selectFitness(*originalCh);
ga.sortByFitness(*originalCh);
ga.print(*originalCh);
if((*originalCh)[0].fit == 1) break;
ga.mate(*originalCh,*bufferCh);
ga.swap(*&originalCh, *&bufferCh);
}
std::cin.get();
return 0;
}
