private void CreateDendrites(NeuralChromosome chromosome)
{
foreach (DendriteGene dendrite in chromosome.dendriteGenes)
{
List <Neuron> startingNeurons = new List <Neuron>(); //neurons that will be added as input
List <Neuron> endNeurons = new List <Neuron>(); //neurons that require input
if (inputNeurons.ContainsKey(dendrite.startNeuron))
{
startingNeurons.AddRange(inputNeurons[dendrite.startNeuron]);
}
if (hiddenNeurons.ContainsKey(dendrite.startNeuron))
{
startingNeurons.AddRange(hiddenNeurons[dendrite.startNeuron]);
}
if (outputNeurons.ContainsKey(dendrite.endNeuron))
{
endNeurons.AddRange(outputNeurons[dendrite.endNeuron]);
}
if (hiddenNeurons.ContainsKey(dendrite.endNeuron))
{
endNeurons.AddRange(hiddenNeurons[dendrite.endNeuron]); //hidden neurons can be both start and end
}
foreach (Neuron neuron in endNeurons)
{
neuron.AddInput(startingNeurons, dendrite.weight);//add all starting neurons as input
}
}
}
private static NeuralChromosome Elimination(NeuralChromosome chromosome)
{
if (chromosome.dendriteGenes.Length == 0)
{
return(chromosome);
}
int pos = r.Next(chromosome.dendriteGenes.Length);
DendriteGene[] dGenes = new DendriteGene[chromosome.dendriteGenes.Length - 1];
for (int i = 0; i < pos; i++)
{
dGenes[i] = chromosome.dendriteGenes[i];
}
for (int i = pos; i < dGenes.Length; i++)
{
dGenes[i] = chromosome.dendriteGenes[i + 1];
}
return(new NeuralChromosome(dGenes));
}
public Chromosome(Gene[] genes, NeuralChromosome neuralChromosome, string mutationTracker, ChromosomeParameters parameters)
{
this.Genes = new List <Gene>(genes).ToArray();
this.MutationTracker = mutationTracker;
this.neuralChromosome = neuralChromosome;
this.Parameters = parameters;
}
private static NeuralChromosome InsertNeuron(NeuralChromosome chromosome)
{
List <int> numbers = chromosome.GetNeuronsNumbers();
int newNumber;
if (numbers.Any())
{
newNumber = numbers.Max() + 1;
}
else
{
newNumber = 1;
}
//substitute a existing dendrite
int pos = r.Next(chromosome.dendriteGenes.Length);
DendriteGene dendrite = chromosome.dendriteGenes[pos];
DendriteGene[] dendriteGenes = new DendriteGene[chromosome.dendriteGenes.Length + 1];
for (int i = 0; i < chromosome.dendriteGenes.Length; i++)
{
dendriteGenes[i] = chromosome.dendriteGenes[i];
}
dendriteGenes[chromosome.dendriteGenes.Length] = new DendriteGene(newNumber, dendriteGenes[pos].endNeuron, dendriteGenes[pos].weight);
dendriteGenes[pos] = new DendriteGene(dendrite.startNeuron, newNumber, 1f);
return(new NeuralChromosome(dendriteGenes));
}
/// <summary>
/// Runs the evolution process.
/// </summary>
/// <param name="evolveSpecies">A delegate that evolves the species subpopulations.</param>
private void RunEvolution(Action evolveSpecies)
{
NeuralChromosome.ResetAllInnovationTrackers();
while (true)
{
evolveSpecies();
_optimalSpecies = FindOptimalSpecies();
_generation++;
if (_loggingEnabled)
{
_logger.Log(_generation, ((Creature)OptimalCreature).TrueFitness, OptimalCreature.Fitness, _optimalSpecies.Niches);
}
if (_populationMetadata.TerminationConditions.Any(condition => condition.Check(this)))
{
foreach (var species in _species)
{
foreach (var creature in species)
{
((Creature)creature).UnregisterNiche();
}
}
break;
}
}
}
private static NeuralChromosome WeightModification(NeuralChromosome chromosome)
{
if (chromosome.dendriteGenes.Length == 0)
{
return(chromosome);
}
return(ModifyWeights(chromosome));
}
/// <summary>
/// Creates the neural network by linking the hidden neurons (as described in the chromosome )to the input and output layer
/// </summary>
/// <param name="chromosome">chromosome from which to create the NN</param>
/// <param name="inputLayer">Neurons that give input to the neural network</param>
/// <param name="outputLayer">neurons that are in the output layer of the neural network</param>
public void BuildFromChromosome(NeuralChromosome chromosome, Neuron[] inputLayer, Neuron[] outputLayer)
{
//Create all the neurons
CreateHiddenNeurons(chromosome, inputLayer, outputLayer);
//Add links between neurons
CreateDendrites(chromosome);
output = ConvertToArray(outputNeurons);
hidden = ConvertToArray(hiddenNeurons);
}
private static NeuralChromosome StructureModification(NeuralChromosome chromosome, HashSet <int> inputNeuronsNumbers, HashSet <int> outputNeuronsNumbers)
{
if (chromosome.dendriteGenes.Length > 0 && r.NextDouble() < 0.35)
{
if (r.NextDouble() < 0.1)
{
return(Elimination(chromosome));
}
return(InsertNeuron(chromosome));
}
return(InsertDendrite(chromosome, inputNeuronsNumbers, outputNeuronsNumbers));
}
private static NeuralChromosome InsertDendrite(NeuralChromosome chromosome, HashSet <int> inputNeuronsNumbers, HashSet <int> outputNeuronsNumbers)
{
HashSet <int> startingNeurons = new HashSet <int>(); //neurons that will be added as input
HashSet <int> endNeurons = new HashSet <int>(); //neurons that require input
List <int> neuronsInChromosome = chromosome.GetNeuronsNumbers();
startingNeurons.UnionWith(inputNeuronsNumbers);
startingNeurons.UnionWith(neuronsInChromosome);
foreach (int n in outputNeuronsNumbers)
{
startingNeurons.Remove(n);
}
endNeurons.UnionWith(outputNeuronsNumbers);
endNeurons.UnionWith(neuronsInChromosome);
foreach (int n in inputNeuronsNumbers)
{
endNeurons.Remove(n);
}
if (!startingNeurons.Any() || !endNeurons.Any())
{
return(chromosome);
}
int startingNeuron = startingNeurons.ToArray()[r.Next(startingNeurons.Count)];
int endingNeuron = endNeurons.ToArray()[r.Next(endNeurons.Count)];
HashSet <DendriteGene.ComparableGene> existingGenes = new HashSet <DendriteGene.ComparableGene>();
foreach (DendriteGene dendrite in chromosome.dendriteGenes)
{
existingGenes.Add(dendrite.GetComparableGene());
}
if (existingGenes.Contains(new DendriteGene.ComparableGene(new DendriteGene(startingNeuron, endingNeuron, 0))))
{
return(chromosome);
}
DendriteGene[] dendriteGenes = new DendriteGene[chromosome.dendriteGenes.Length + 1];
for (int i = 0; i < chromosome.dendriteGenes.Length; i++)
{
dendriteGenes[i] = chromosome.dendriteGenes[i];
}
dendriteGenes[chromosome.dendriteGenes.Length] = new DendriteGene(startingNeuron, endingNeuron, (float)r.NextDouble() * 2 - 1);
return(new NeuralChromosome(dendriteGenes));
}
private static NeuralChromosome ModifyWeights(NeuralChromosome chromosome)
{
DendriteGene[] dendriteGenes = new DendriteGene[chromosome.dendriteGenes.Length];
for (int i = 0; i < dendriteGenes.Length; i++)
{
dendriteGenes[i] = new DendriteGene(chromosome.dendriteGenes[i].startNeuron, chromosome.dendriteGenes[i].endNeuron, ModifyWeight(chromosome.dendriteGenes[i].weight));
}
/*
* int pos = r.Next(dendriteGenes.Length);
* dendriteGenes[pos] = new DendriteGene(dendriteGenes[pos].startNeuron, dendriteGenes[pos].endNeuron, ModifyWeight(dendriteGenes[pos].weight));
*/
chromosome = new NeuralChromosome(dendriteGenes);
return(chromosome);
}
/// <summary>
/// Creates all hidden neurons and links them to the input and output layer. Fills
/// the dictionaries variables
/// </summary>
/// <param name="chromosome"></param>
/// <param name="inputLayer">Neurons that comes from the organism and can be used as input in the neural network</param>
/// <param name="outputLayer">Neurons that will be used by the organism to make decisions, are the output of the neural network</param>
private void CreateHiddenNeurons(NeuralChromosome chromosome, Neuron[] inputLayer, Neuron[] outputLayer)
{
List <int> hiddenNeuronsNumbers = chromosome.GetNeuronsNumbers();
foreach (Neuron neuron in inputLayer)
{
AddNeuron(inputNeurons, neuron);
hiddenNeuronsNumbers.Remove(neuron.number);
}
foreach (Neuron neuron in outputLayer)
{
AddNeuron(outputNeurons, neuron);
hiddenNeuronsNumbers.Remove(neuron.number);
}
foreach (int hn in hiddenNeuronsNumbers)
{
Neuron neuron = new Neuron(hn);
AddNeuron(hiddenNeurons, neuron);
}
}
public static Chromosome CreateCrossover(Chromosome ch1, Chromosome ch2)
{
Gene[] genes = CrossoverManager.Crossover(ch1.Genes, ch2.Genes);
var neuralComparableGenes = CrossoverManager.Crossover(ch1.neuralChromosome.GetComparableGenes(), ch2.neuralChromosome.GetComparableGenes());
DendriteGene[] dendriteGenes = new DendriteGene[neuralComparableGenes.Length];
for (int i = 0; i < dendriteGenes.Length; i++)
{
dendriteGenes[i] = neuralComparableGenes[i].GetGene();
}
NeuralChromosome neuralChromosome = new NeuralChromosome(dendriteGenes);
ChromosomeParameters parameters = CrossoverManager.CrossParameters(ch1.Parameters, ch2.Parameters);
Chromosome chromosome = new Chromosome(genes, neuralChromosome, ch1.MutationTracker + "CR", parameters);
return(chromosome);
}
public double getScore()
{
NeuralChromosome c = (NeuralChromosome)GetChromosome(0);
return(c.Cost);
}
private NeuralChromosome GetCrossover(NeuralChromosome x, NeuralChromosome y, NeuralCrossover crossover)
{
var rator = GeneticFactory.ConstructNeuralCrossoverOperators(crossover).First();
return(rator.Invoke(x, y) as NeuralChromosome);
}
private NeuralChromosome GetMutation(NeuralChromosome chromosome, NeuralMutation mutation)
{
var rator = GeneticFactory.ConstructNeuralMutationOperators(mutation).First();
return(rator.Invoke(chromosome) as NeuralChromosome);
}
