public double MeasureDiversity(Utility.SortList <AIPlayer> individuals)
{
int size = individuals.Count;
double[] fitnessValues = new double[size];
for (int i = 0; i < size; i++)
{
fitnessValues[i] = individuals[i].GetFitness();
}
return(fitnessValues.Distinct().Count() / (double)size);
}
public double MeasureDiversity(Utility.SortList <AIPlayer> individuals)
{
double avgFitness = individuals.Average(i => i.GetFitness());
double fitDiff = 0.0;
foreach (AIPlayer p in individuals)
{
double fit = p.GetFitness();
fitDiff += (fit - avgFitness) * (fit - avgFitness);
}
return(Math.Sqrt(fitDiff / (individuals.Count - 1)));
}
public double MeasureDiversity(Utility.SortList <AIPlayer> individuals)
{
int maxDist = individuals.Get(0).Chromosome.Length;
int count = individuals.Count;
double sum = 0;
for (int i = 0; i < count; i++)
{
for (int p = i + 1; p < count; p++)
{
sum += LevenshteinDistance(individuals.Get(i).Chromosome.Bitstring, individuals.Get(p).Chromosome.Bitstring);
}
}
//calculate average distance between any two strings, and normalize to lie in range 0.0-1.0
return((sum / (count * (count - 1) / 2)) / maxDist);
}
public double MeasureDiversity(Utility.SortList <AIPlayer> individuals)
{
//calculate average hamming distance between any two strings
int size = individuals.Count;
int hammingTotal = 0;
for (int i = 0; i < size; i++)
{
for (int p = i + 1; p < size; p++)
{
hammingTotal += SingleHammingDistance(individuals.Get(i).Chromosome.Bitstring, individuals.Get(p).Chromosome.Bitstring);
}
}
//calculate average between any two strings, and normalize to lie in range 0.0-1.0
int maxVal = individuals.Get(0).Chromosome.Bitstring.Length;
return((hammingTotal / (double)(size * (size - 1) / 2)) / maxVal);
}
