private void SkrgicSelection(int size)
{
// new chromosomes, initially empty
List <GPChromosome> newPopulation = new List <GPChromosome>();
// double k = 0.2; //additionalParameter;
double fitnessMax = chromosomes.Max(x => x.fitness) * (1.0 + gpParameters.SelParam1);
for (int i = 0; i < size; i++)
{
//Slucajni index iz populacije
int randomIndex = Globals.radn.Next(0, chromosomes.Count);
//Slucajni broj izmedju 0-maxFitnes ukljucujuci i maxFitness koje je prethodno vec izracunat kod evaluacije hromosoma
double randomFitness = Globals.radn.NextDouble(0, fitnessMax /*, true include MaxValue*/);
while (true)
{
//Akoje slucajno generirani broj manji ili jednak fitnesu slucajnog hromosoma selektuj hromosom
if (randomFitness <= chromosomes[randomIndex].fitness * (1.0 + gpParameters.SelParam1 / fitnessMax))
{
newPopulation.Add(chromosomes[randomIndex].Clone());
break;
}
randomIndex = Globals.radn.Next(0, chromosomes.Count);
randomFitness = Globals.radn.NextDouble(0, fitnessMax /*, true include MaxValue*/);
}
}
// survived chromosomes
chromosomes.AddRange(newPopulation);
// old population is goint to die
GPChromosome.DestroyChromosomes(ref chromosomes);
}
private void FUSSSelection(int size)
{
// new chromosomes, initially empty
List <GPChromosome> newPopulation = new List <GPChromosome>();
//Maximum fitness
double fitnessMax = chromosomes.Max(x => x.fitness);
double rnd;
double dif;
int selIndex = 0;
for (int j = 0; j < size; j++)
{
rnd = Globals.radn.NextDouble(0, fitnessMax);
dif = Math.Abs(chromosomes[0].fitness - rnd);
selIndex = 0;
for (int i = 1; i < chromosomes.Count; i++)
{
double curDif = Math.Abs(chromosomes[i].fitness - rnd);
if (dif > curDif)
{
dif = curDif;
selIndex = i;
}
}
newPopulation.Add(chromosomes[selIndex].Clone());
}
// survived chromosomes
chromosomes.AddRange(newPopulation);
// old population is goint to die
GPChromosome.DestroyChromosomes(ref chromosomes);
}
private void UniversalStohasticSelection(int size)
{
// new chromosomes, initially empty
List <GPChromosome> newPopulation = new List <GPChromosome>();
//chromosomes.Sort();
int currentSize = chromosomes.Count();
float fitnessSum = chromosomes.Sum(c => c.fitness);
// get random distance value
float randDist = (float)Globals.radn.NextDouble(0, 1.0 / (double)size);
float partFitnes = 0;
for (int j = 0; j < size; j++)
{
partFitnes = 0;
for (int i = 0; i < chromosomes.Count; i++)
{
partFitnes += chromosomes[i].fitness / fitnessSum;
if (randDist <= partFitnes)
{
newPopulation.Add(chromosomes[i].Clone());
break;
}
}
randDist += 1.0F / (float)size;
}
// survived chromosomes
chromosomes.AddRange(newPopulation);
// old population is goint to die
GPChromosome.DestroyChromosomes(ref chromosomes);
}
private void TournamentSelection(int size)
{
// velicinaPopulacije of current chromosomes
int currentSize = chromosomes.Count;
List <GPChromosome> tourn = new List <GPChromosome>((int)gpParameters.SelParam1);
// new chromosomes, initially empty
List <GPChromosome> newPopulation = new List <GPChromosome>();
for (int j = 0; j < size; j++)
{
currentSize = chromosomes.Count;
for (int i = 0; i < gpParameters.SelParam1 && i < currentSize; i++)
{
int ind = Globals.radn.Next(currentSize);
tourn.Add(chromosomes[ind]);
}
tourn.Sort();
newPopulation.Add(tourn[0].Clone());
chromosomes.Remove(tourn[0]);
tourn.Clear();
}
// survived chromosomes
chromosomes.AddRange(newPopulation);
// old population is goint to die
GPChromosome.DestroyChromosomes(ref chromosomes);
}
/// <summary>
/// Fitness šproportionate selection.
/// </summary>
/// <param name="size"></param>
private void FitnessProportionateSelection(int size)
{
// new chromosomes, initially empty
List <GPChromosome> newPopulation = new List <GPChromosome>();
int currentSize = chromosomes.Count;
double sumOfFitness = 0;
//calculate sum of fitness
for (int i = 0; i < currentSize; i++)
{
sumOfFitness += chromosomes[i].fitness;
}
// create wheel ranges
double[] rangeMax = new double[currentSize];
double s = 0;
for (int i = 0; i < currentSize; i++)
{
// cumulative normalized fitness
s += (chromosomes[i].fitness / sumOfFitness);
rangeMax[i] = s;
}
// select chromosomes from old chromosomes to the new chromosomes
for (int j = 0; j < size; j++)
{
// get wheel value
double wheelValue = Globals.radn.NextDouble();
// find the chromosome for the wheel value
for (int i = 0; i < currentSize; i++)
{
//double wheelValue = rand.NextDouble();
if (wheelValue <= rangeMax[i] && chromosomes[i].fitness != -1)
{
// add the chromosome to the new population
var ch = chromosomes[i].Clone();
newPopulation.Add(ch);
chromosomes[i].fitness = -1;
break;
}
}
}
// survived chromosomes
chromosomes.AddRange(newPopulation);
// old population is goint to die
GPChromosome.DestroyChromosomes(ref chromosomes);
}
private void RankSelection(int size)
{
// new chromosomes, initially empty
List <GPChromosome> newPopulation = new List <GPChromosome>();
// velicinaPopulacije of current chromosomes
int currentSize = chromosomes.Count;
// sort current chromosomes
//chromosomes.Sort();
// calculate amount of ranges in the wheel
double ranges = currentSize * (currentSize + 1) / 2;
// create wheel ranges
double[] rangeMax = new double[currentSize];
double s = 0;
for (int i = 0, n = currentSize; i < currentSize; i++, n--)
{
s += ((double)n / ranges);
rangeMax[i] = s;
}
// select chromosomes from old chromosomes to the new chromosomes
for (int j = 0; j < size; j++)
{
// get wheel value
double wheelValue = Globals.radn.NextDouble();
// find the chromosome for the wheel value
for (int i = 0; i < currentSize; i++)
{
// get wheel value
if (wheelValue <= rangeMax[i])
{
// add the chromosome to the new chromosomes
newPopulation.Add(chromosomes[i].Clone());
break;
}
//Debug.Assert(false);
}
}
// survived chromosomes
chromosomes.AddRange(newPopulation);
// old population is goint to die
GPChromosome.DestroyChromosomes(ref chromosomes);
}
