/// <summary>
/// Takes 2 parent gene vectors, selects a midpoint and then swaps the ends
/// of each genome creating 2 new genomes which are stored in baby1 and
/// baby2.
/// </summary>
/// <param name="mum"></param>
/// <param name="dad"></param>
/// <param name="baby1"></param>
/// <param name="baby2"></param>
private void Crossover(List <Gene> mum, List <Gene> dad, ref List <Gene> baby1, ref List <Gene> baby2)
{
//just return parents as offspring dependent on the rate
//or if parents are the same
if ((GAUtils.RandDouble() > CrossoverRate) || (mum == dad))
{
baby1 = mum;
baby2 = dad;
return;
}
//determine a crossover point
int crossOverPoint = GAUtils.RandInt(0, mum.Count - 1); // = 29
for (int i = 0; i < mum.Count; i++)
{
var crossed = Gene.Cross(mum[i], dad[i], i < crossOverPoint);
baby1.Add(crossed[0]);
baby2.Add(crossed[1]);
}
// swap the bits
// for (int i = 0; i < crossOverPoint; ++i)
// {
// baby1.Add(mum[i]);
// baby2.Add(dad[i]);
// }
// for (int i = crossOverPoint; i < mum.Count; ++i)
// {
// baby1.Add(dad[i]);
// baby2.Add(mum[i]);
// }
}
/// <summary>
/// iterates through each genome flipping the bits acording to the
/// mutation rate
/// </summary>
/// <param name="genes"></param>
private void Mutate(List <Gene> genes)
{
// Go through each gene
for (int i = 0; i < genes.Count; i++)
{
Gene gene = genes[i];
for (int j = 0; j < gene.Bits.Count; j++)
{
//do we flip this bit?
if (GAUtils.RandDouble() < MutationRate)
{
//flip the bit
gene.Bits[j] = gene.Bits[j] == 0 ? 1 : 0;
}
}
}
}
/// <summary>
/// selects a member of the population by using roulette wheel
/// selection as described in the text.
/// </summary>
/// <returns></returns>
private TGenome RouletteWheelSelection()
{
double fSlice = GAUtils.RandDouble() * TotalFitnessScore;
double cfTotal = 0.0;
int selectedGenome = 0;
for (int i = 0; i < Genomes.Count; ++i)
{
cfTotal += Genomes[i].Fitness;
if (cfTotal > fSlice)
{
selectedGenome = i;
break;
}
}
return(Genomes[selectedGenome]);
}