// create weight at network initialization
public void CreateStartWeight(int neuronIn, int neuronOut, float value)
{
var oldWeightInnovID = innovationDB.CheckWeightInnovation(neuronIn, neuronOut);
Weight newWeight;
// new innovation
if (oldWeightInnovID == -1)
{
var weightInnov = innovationDB.CreateInnovation(InnovationDB.Innovation.Type.NEW_WEIGHT, neuronIn, neuronOut, Neuron.Type.NONE, -1, -1);
newWeight = new Weight(weightInnov.ID, neuronIn, neuronOut, value, true, false);
}
else
{
newWeight = new Weight(oldWeightInnovID, neuronIn, neuronOut, value, true, false);
}
weights.Add(newWeight.innovID, newWeight);
}
public void MutateEnableDisable(bool enable)
{
var candidates = new IntWeightDictionary();
for (int i = 0; i < weights.Count; i++)
{
var weight = weights.ElementAt(i).Value;
if (weight.enabled != enable)
{
candidates.Add(i, weight);
}
}
if (candidates.Count == 0)
{
return;
}
var chosen = candidates.ElementAt(Random.Range(0, candidates.Count)).Value;
chosen.enabled = enable;
}
public Genome Crossover(Genome other)
{
var parentA = this;
var parentB = other;
Genome best = new Genome();
if (parentA.fitness == parentB.fitness)
{
// if same fitness pick shortest genome
if (parentA.NumGenes() < parentB.NumGenes())
{
best = parentA;
}
else if (parentB.NumGenes() < parentA.NumGenes())
{
best = parentB;
}
else
{
int randomElement = Random.Range(0, 2);
best = randomElement == 0 ? parentA : parentB;
}
}
else if (parentA.fitness > parentB.fitness)
{
best = parentA;
}
else
{
best = parentB;
}
var childWeights = new IntWeightDictionary();
var childNeurons = new IntNeuronDictionary();
int parentAIndex = 0, parentBIndex = 0;
Weight parentAWeight = null, parentBWeight = null;
// while end of both genes is not reached
while (parentAIndex < parentA.weights.Count || parentBIndex < parentB.weights.Count)
{
if (parentAIndex < parentA.weights.Count)
{
parentAWeight = parentA.weights.ElementAt(parentAIndex).Value.Copy();
}
if (parentBIndex < parentB.weights.Count)
{
parentBWeight = parentB.weights.ElementAt(parentBIndex).Value.Copy();
}
Weight selectedWeight = null;
if (parentAIndex == parentA.weights.Count && parentBIndex != parentB.weights.Count)
{
// add excess genes
if (parentB == best)
{
selectedWeight = parentBWeight;
}
parentBIndex++;
}
else if (parentBIndex == parentB.weights.Count && parentAIndex != parentA.weights.Count)
{
// add excess genes
if (parentA == best)
{
selectedWeight = parentAWeight;
}
parentAIndex++;
}
else if (parentAWeight.innovID < parentBWeight.innovID)
{
// add parentA disjoint genes
if (parentA == best)
{
selectedWeight = parentAWeight;
}
parentAIndex++;
}
else if (parentBWeight.innovID < parentAWeight.innovID)
{
// add parentB disjoint genes
if (parentB == best)
{
selectedWeight = parentBWeight;
}
parentBIndex++;
}
else
{
int randomElement = Random.Range(0, 2);
selectedWeight = randomElement == 0 ? parentAWeight : parentBWeight;
if (!parentAWeight.enabled && !parentBWeight.enabled)
{
float random = Random.value;
if (random <= pWeightEnabled)
{
selectedWeight.enabled = true;
}
}
parentAIndex++;
parentBIndex++;
}
if (selectedWeight != null && !childWeights.ContainsKey(selectedWeight.innovID))
{
childWeights.Add(selectedWeight.innovID, selectedWeight);
}
if (selectedWeight != null)
{
if (!childNeurons.ContainsKey(selectedWeight.neuronIn))
{
Neuron neuron = null;
if (parentA.weights.Any(x => x.Value.neuronIn == selectedWeight.neuronIn))
{
neuron = parentA.neurons[selectedWeight.neuronIn].Copy();
}
else if (parentB.weights.Any(x => x.Value.neuronIn == selectedWeight.neuronIn))
{
neuron = parentB.neurons[selectedWeight.neuronIn].Copy();
}
if (neuron == null)
{
Debug.LogError("NEURON IS NULL!");
}
childNeurons.Add(neuron.ID, neuron);
}
if (!childNeurons.ContainsKey(selectedWeight.neuronOut))
{
Neuron neuron = null;
if (parentA.weights.Any(x => x.Value.neuronOut == selectedWeight.neuronOut))
{
neuron = parentA.neurons[selectedWeight.neuronOut].Copy();
}
else if (parentB.weights.Any(x => x.Value.neuronOut == selectedWeight.neuronOut))
{
neuron = parentB.neurons[selectedWeight.neuronOut].Copy();
}
if (neuron == null)
{
Debug.LogError("NEURON IS NULL!");
}
childNeurons.Add(neuron.ID, neuron);
}
}
}
return(new Genome(childNeurons, childWeights));
}