public Species Produce(Species parent1, Species parent2)
{
BitArray bitArray = new BitArray(parent1.Values.Length);
for (var i = 0; i < bitArray.Count; i++)
{
bitArray[i] = _rnd.Next(0, 2) == 1 ? parent1.Values[i] : parent2.Values[i];
}
var result = new Species(bitArray, _fitnessFun.Calculate(bitArray));
return(result);
}
public Species Produce(Species parent1, Species parent2)
{
BitArray bitArray = new BitArray(parent1.Values.Length);
var better = parent1.Fittness >= parent2.Fittness ? parent1 : parent2;
var worse = better == parent1 ? parent2 : parent1;
for (var i = 0; i < bitArray.Count; i++)
{
bitArray[i] = _rnd.NextDouble() >= 1 - _betterParentGeneSelectionProbability
? better.Values[i]
: worse.Values[i];
}
var result = new Species(bitArray, _fitnessFun.Calculate(bitArray));
return(result);
}
private static Species[] GenerateInitialSpecies(int speciesNumber, int itemCount, IFitnessFun fitnessFun)
{
var rnd = new Random();
return(Enumerable.Range(1, speciesNumber)
.Select(_ =>
{
var vector = new BitArray(itemCount);
for (var i = 0; i < vector.Count; i++)
{
bool value = rnd.Next(0, 2) == 1;
vector[i] = value;
}
return vector;
})
.Select(v => new Species(v, fitnessFun.Calculate(v)))
.ToArray());
}