/// <summary>
/// This constructor copys a gene and then updates the inputs and outputs
/// </summary>
/// <param name="gene"></param>
private Gene(Gene gene)
{
NodeInput = gene.NodeInput;
NodeOutput = gene.NodeOutput;
ConnectionEnabled = gene.ConnectionEnabled;
ConnectionWeight = gene.ConnectionWeight;
InnovationNumber = gene.InnovationNumber;
}
/// <summary>
/// This method adds a single new node as a mutation and handles a connection 'split'.
/// </summary>
private void MutateAddNode()
{
// Find an available connection to modify
// Get the list of available genes to mutate
Gene[] availableGeneArray = Genes.Where(item => item.ConnectionEnabled).ToArray();
if (availableGeneArray.Count() == 0)
return;
Gene oldGene = availableGeneArray[rng.Next(availableGeneArray.Count())];
// Create a new node (find the maximum node value and add it)
int newNodeIndex = Math.Max(Genes.Select(g => g.NodeInput).Union(Genes.Select(g => g.NodeOutput)).Max(), evoConfig.InputNodeCount + evoConfig.OutputNodeCount) + 1;
// Add a new start value if it does not yet exist
if (!startValues.ContainsKey(newNodeIndex))
startValues.Add(newNodeIndex, 2 - evoConfig.rng.NextDouble() * 4);
// First gene (connection) copys the original, but is given an new innovation number
Gene g1 = (Gene) oldGene.Clone();
g1.NodeOutput = newNodeIndex;
g1.InnovationNumber = Innovation.NewInnovationNumber;
// Second connection is treated as a passthrough (with a sigmoid function)
Gene g2 = new Gene(evoConfig, newNodeIndex, oldGene.NodeOutput);
// Disable the oldGene
oldGene.ConnectionEnabled = false;
// Add new genes to the list
Genes.Add(g1);
Genes.Add(g2);
}
/// <summary>
/// This method adds a single new gene (connection) as a mutation.
/// </summary>
private void MutateAddGene(bool biasOnly = false)
{
IEnumerable<int> allNodes = Genes.Select(g => g.NodeInput)
.Union(
Genes.Select(g => g.NodeOutput))
.Distinct()
.Where(n => !(NeuralNetwork.IsInputOutputNode(evoConfig, n)));
List<int> availableInputNodeList = allNodes.Union(Enumerable.Range(0, evoConfig.InputNodeCount + 1)).ToList();
List<int> availableOutputNodeList = allNodes.Union(Enumerable.Range(evoConfig.InputNodeCount + 1, evoConfig.OutputNodeCount)).ToList();
// Select a Node
int n1 = (biasOnly) ? evoConfig.InputNodeCount : availableInputNodeList[rng.Next(availableInputNodeList.Count())];
// Remove this node from the list
if (availableOutputNodeList.Contains(n1))
availableOutputNodeList.Remove(n1);
// Select a Node After
int n2 = availableOutputNodeList[rng.Next(availableOutputNodeList.Count())];
// Create new gene
Gene newGene = new Gene(evoConfig, n1, n2);
newGene.ConnectionWeight = 2 - evoConfig.rng.NextDouble() * 4;
// Insert new gene
Genes.Add(newGene);
}