private SGenome GetChromoRoulette()
{
//generate a random number between 0 & total fitness count
double Slice = (double)(m_Rand.NextDouble() * m_dTotalFitness);
//this will be set to the chosen chromosome
SGenome TheChosenOne = m_vecPop[0];
//go through the chromosones adding up the fitness so far
double FitnessSoFar = 0;
for (int i = 0; i < m_iPopSize; ++i)
{
FitnessSoFar += m_vecPop[i].Fitness;
//if the fitness so far > random number return the chromo at
//this point
if (FitnessSoFar >= Slice)
{
TheChosenOne = m_vecPop[i];
break;
}
}
return(TheChosenOne);
}
//this runs the GA for one generation.
public List <SGenome> Epoch(List <SGenome> old_pop)
{
//assign the given population to the classes population
m_vecPop = old_pop;
//reset the appropriate variables
Reset();
//sort the population (for scaling and elitism)
m_vecPop.Sort();
//sort(m_vecPop.begin(), m_vecPop.end());
//calculate best, worst, average and total fitness
CalculateBestWorstAvTot();
//create a temporary vector to store new chromosones
List <SGenome> vecNewPop = new List <SGenome>();
//Now to add a little elitism we shall add in some copies of the
//fittest genomes. Make sure we add an EVEN number or the roulette
//wheel sampling will crash
if ((m_NumCopiesElite * m_NumElite % 2) == 0)
{
GrabNBest(m_NumElite, m_NumCopiesElite, vecNewPop);
}
//now we enter the GA loop
//repeat until a new population is generated
while (vecNewPop.Count < m_iPopSize)
{
//grab two chromosones
SGenome mum = GetChromoRoulette();
SGenome dad = GetChromoRoulette();
//create some offspring via crossover
List <double> baby1 = new List <double>();
List <double> baby2 = new List <double>();
Crossover(mum.Weights, dad.Weights, baby1, baby2);
//now we mutate
Mutate(baby1);
Mutate(baby2);
//now copy into vecNewPop population
vecNewPop.Add(new SGenome(baby1, 0));
vecNewPop.Add(new SGenome(baby2, 0));
}
//finished so assign new pop back into m_vecPop
m_vecPop = vecNewPop;
return(m_vecPop);
}