private static Genome crossDominant(NeatManager neat, Genome dominant, Genome other)
{
if (dominant.synapses.Count == 0 || other.synapses.Count == 0)
{
return(null);
}
int similarityNeurons = -1;
for (int i = 1; i < 100000; i++)
{
if (dominant.hasSynapse(i) && other.hasSynapse(i))
{
similarityNeurons = i;
}
}
if (similarityNeurons == -1)
{
throw new Exception("No similarity between Genomes");
}
Genome newGenome = new Genome(neat, null, dominant.inputNeurons.ToArray(), dominant.outputNeurons.ToArray());
for (int i = 1; i <= dominant.getHighestInnovationNumber(); i++)
{
if (dominant.hasSynapse(i))
{
int innovationNumber, inputNeuron, outputNeuron;
double weigth;
bool enabled;
if (other.hasSynapse(i))
{
Random r = new Random();
if (r.Next(0, 1) == 1)
{
innovationNumber = dominant.synapses[i].innovationNumber;
inputNeuron = dominant.synapses[i].from;
outputNeuron = dominant.synapses[i].to;
weigth = dominant.synapses[i].weight;
enabled = dominant.synapses[i].enabled;
}
else
{
innovationNumber = other.synapses[i].innovationNumber;
inputNeuron = other.synapses[i].from;
outputNeuron = other.synapses[i].to;
weigth = other.synapses[i].weight;
enabled = other.synapses[i].enabled;
}
}
else
{
innovationNumber = dominant.synapses[i].innovationNumber;
inputNeuron = dominant.synapses[i].from;
outputNeuron = dominant.synapses[i].to;
weigth = dominant.synapses[i].weight;
enabled = dominant.synapses[i].enabled;
}
newGenome.addSynapse(new Synapse(innovationNumber, inputNeuron, outputNeuron, weigth, enabled), dominant, other);
}
}
newGenome.removeDuplicateSynapses();
newGenome.mutate();
return(newGenome);
}
// REF: [GITHUB] SanderGielisse logic for distance
// between genomes (eg compatibility)
// (https://github.com/SanderGielisse/Mythan)
public static double distance(Genome a, Genome b)
{
int aLength = a.getHighestInnovationNumber();
int bLength = b.getHighestInnovationNumber();
Genome longest;
Genome shortest;
if (aLength > bLength)
{
longest = a;
shortest = b;
}
else
{
longest = b;
shortest = a;
}
int shortestLength = shortest.getHighestInnovationNumber();
int longestLength = longest.getHighestInnovationNumber();
double disjoint = 0;
double excess = 0;
List <double> weights = new List <double>();
for (int i = 0; i < longestLength; i++)
{
Synapse aa = longest.getSynapse(i);
Synapse bb = shortest.getSynapse(i);
if ((aa == null && bb != null) || (aa != null && bb == null))
{
// only present in one of them
if (i <= shortestLength)
{
disjoint++;
}
else if (i > shortestLength)
{
excess++;
}
}
if (aa != null && bb != null)
{
double distance = Math.Abs(aa.weight - bb.weight);
weights.Add(distance);
}
}
double total = 0;
double size = 0;
foreach (double w in weights)
{
total += w;
size++;
}
double averageWeightDistance = total / size;
double n = longest.synapses.Count;
double c1 = Config.DISTANCE_EXCESS_WEIGHT;
double c2 = Config.DISTANCE_DISJOINT_WEIGHT;
double c3 = Config.DISTANCE_WEIGHTS_WEIGHT;
double d = ((c1 * excess) / n) + ((c2 * disjoint) / n) + (c3 * averageWeightDistance);
return(d);
}
