//-----------------------------------Epoch()-----------------------------
//
// takes a population of chromosones and runs the algorithm through one
// cycle.
// Returns a new population of chromosones.
//
//-----------------------------------------------------------------------
vector<SGenome> CGenAlg::Epoch(vector<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)
sort(m_vecPop.begin(), m_vecPop.end());
//calculate best, worst, average and total fitness
CalculateBestWorstAvTot();
//create a temporary vector to store new chromosones
vector <SGenome> vecNewPop;
//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 (!(CParams::iNumCopiesElite * CParams::iNumElite % 2))
{
GrabNBest(CParams::iNumElite, CParams::iNumCopiesElite, vecNewPop);
}
//now we enter the GA loop
//repeat until a new population is generated
while (vecNewPop.size() < m_iPopSize)
{
//grab two chromosones
SGenome mum = GetChromoRoulette();
SGenome dad = GetChromoRoulette();
//create some offspring via crossover
vector<double> baby1, baby2;
Crossover(mum.vecWeights, dad.vecWeights, baby1, baby2);
//now we mutate
Mutate(baby1);
Mutate(baby2);
//now copy into vecNewPop population
vecNewPop.push_back(SGenome(baby1, 0));
vecNewPop.push_back(SGenome(baby2, 0));
}
//finished so assign new pop back into m_vecPop
m_vecPop = vecNewPop;
return m_vecPop;
}
TArray<FGenome> GeneticAlg::Epoch(TArray<FGenome> &OldPop)
{
PopArray = OldPop;
Reset();
PopArray.Sort(); //sorts Fgenomes by fitness
CalculateBestWorstAvTot();
TArray<FGenome> NewPop;
if (!(NumCopiesElite * NumElites % 2))
{
GrabNBest(NumElites, NumCopiesElite, NewPop);
}
while (NewPop.Num() < PopulationSize)
{
//grab two chromosones
FGenome mum = GetChromoRoulette();
FGenome dad = GetChromoRoulette();
//create some offspring via crossover
TArray<float> baby1, baby2;
CrossOver(mum.WeightsArray, dad.WeightsArray, baby1, baby2);
//now we mutate
Mutate(baby1);
Mutate(baby2);
//now copy into vecNewPop population
NewPop.Add(FGenome(baby1, 0));
NewPop.Add(FGenome(baby2, 0));
}
//finished so assign new pop back into m_vecPop
PopArray = NewPop;
return PopArray;
}