public Population(int inCount, int outCount, bool bias)
{
if (bias)
{
inCount++;
}
for (int i = 0; i < Amount; i++)
{
Genom genom = new Genom(inCount, outCount);
for (int k = 0; k < inCount; k++)
{
if (k == inCount - 1 && bias)
{
genom.Inputs[k].OldValue = 1;
continue;
}
for (int j = 0; j < outCount; j++)
{
genom.AddLink(genom.Inputs[k], genom.Outputs[j], rnd.NextDouble() * 2 - 1);
}
}
AllNets.Add(genom);
}
Speciate();
}
public Genom Mate(Genom other, Random rnd)
{
Genom child = new Genom(Inputs.Count, Outputs.Count);
bool thisGeneBetter = Fitness > other.Fitness;
List <Link> bestGenes = thisGeneBetter ? LocalLinks : other.LocalLinks;
List <Link> worstGenes = !thisGeneBetter ? LocalLinks : other.LocalLinks;
if (Fitness == other.Fitness)
{
worstGenes.ForEach(g =>
{
if (!bestGenes.Contains(g))
{
bestGenes.Add(g);
}
});
}
foreach (Link link in bestGenes)
{
Node input = new Node(link.Input.Id);
if (!child.LocalNodes.Contains(input))
{
child.Hidden.Add(input);
child.LocalNodes.Add(input);
}
else
{
input = child.LocalNodes.Find(n => n.Equals(input));
}
Node output = new Node(link.Output.Id);
if (!child.LocalNodes.Contains(output))
{
child.Hidden.Add(output);
child.LocalNodes.Add(output);
}
else
{
output = child.LocalNodes.Find(n => n.Equals(output));
}
double weight = link.Output.Ancestors[link.Input];
if (rnd.Next(2) == 0 && worstGenes.Contains(link))
{
Link worseLink = worstGenes.Find(l => l.Equals(link));
weight = worseLink.Output.Ancestors[worseLink.Input];
}
child.AddLink(input, output, weight);
}
return(child);
}
