/// <inheritdoc/>
public void PerformOperation(EncogRandom rnd, IGenome[] parents, int parentIndex,
IGenome[] offspring, int offspringIndex)
{
IArrayGenome parent = (IArrayGenome)parents[parentIndex];
offspring[offspringIndex] = this.owner.Population.GenomeFactory.Factor();
IArrayGenome child = (IArrayGenome)offspring[offspringIndex];
child.Copy(parent);
int length = parent.Size;
int iswap1 = (int)(rnd.NextDouble() * length);
int iswap2 = (int)(rnd.NextDouble() * length);
// can't be equal
if (iswap1 == iswap2)
{
// move to the next, but
// don't go out of bounds
if (iswap1 > 0)
{
iswap1--;
}
else
{
iswap1++;
}
}
// make sure they are in the right order
if (iswap1 > iswap2)
{
int temp = iswap1;
iswap1 = iswap2;
iswap2 = temp;
}
child.Swap(iswap1, iswap2);
}
/// <inheritdoc/>
public void PerformOperation(EncogRandom rnd, IGenome[] parents, int parentIndex,
IGenome[] offspring, int offspringIndex)
{
DoubleArrayGenome parent = (DoubleArrayGenome)parents[parentIndex];
offspring[offspringIndex] = parent.Population.GenomeFactory.Factor();
DoubleArrayGenome child = (DoubleArrayGenome)offspring[offspringIndex];
for (int i = 0; i < parent.Size; i++)
{
double value = parent.Data[i];
value += (perturbAmount - (rnd.NextDouble() * perturbAmount * 2));
child.Data[i] = value;
}
}
/// <inheritdoc />
public override ProgramNode CreateNode(EncogRandom rnd, EncogProgram program,
int depthRemaining, IList<EPLValueType> types)
{
int actualDepthRemaining = depthRemaining;
if (rnd.NextDouble() > 0.5)
{
return _fullGenerator.CreateNode(rnd, program,
actualDepthRemaining, types);
}
return _growGenerator.CreateNode(rnd, program,
actualDepthRemaining, types);
}
/// <summary>
/// Choose a parent to favor.
/// </summary>
/// <param name="rnd">Random generator.</param>
/// <param name="mom">The mother.</param>
/// <param name="dad">The father</param>
/// <returns></returns>
private NEATGenome FavorParent(EncogRandom rnd, NEATGenome mom, NEATGenome dad)
{
// first determine who is more fit, the mother or the father?
// see if mom and dad are the same fitness
if (Math.Abs(mom.Score - dad.Score) < EncogFramework.DefaultDoubleEqual)
{
// are mom and dad the same fitness
if (mom.NumGenes == dad.NumGenes)
{
// if mom and dad are the same fitness and have the same number
// of genes,
// then randomly pick mom or dad as the most fit.
if (rnd.NextDouble() > 0.5)
{
return mom;
}
return dad;
}
// mom and dad are the same fitness, but different number of genes
// favor the parent with fewer genes
if (mom.NumGenes < dad.NumGenes)
{
return mom;
}
return dad;
}
// mom and dad have different scores, so choose the better score.
// important to note, better score COULD BE the larger or smaller
// score.
if (_owner.SelectionComparer.Compare(mom, dad) < 0)
{
return mom;
}
return dad;
}
/// <inheritdoc/>
public void PerformOperation(EncogRandom rnd, IGenome[] parents,
int parentIndex, IGenome[] offspring,
int offspringIndex)
{
var mom = (NEATGenome)parents[parentIndex + 0];
var dad = (NEATGenome)parents[parentIndex + 1];
var best = FavorParent(rnd, mom, dad);
var notBest = (best == mom) ? mom : dad;
var selectedLinks = new List<NEATLinkGene>();
var selectedNeurons = new List<NEATNeuronGene>();
int curMom = 0; // current gene index from mom
int curDad = 0; // current gene index from dad
NEATLinkGene selectedGene = null;
// add in the input and bias, they should always be here
int alwaysCount = ((NEATGenome)parents[0]).InputCount
+ ((NEATGenome)parents[0]).OutputCount + 1;
for (int i = 0; i < alwaysCount; i++)
{
AddNeuronId(i, selectedNeurons, best, notBest);
}
while ((curMom < mom.NumGenes) || (curDad < dad.NumGenes))
{
NEATLinkGene momGene = null; // the mom gene object
NEATLinkGene dadGene = null; // the dad gene object
long momInnovation = -1;
long dadInnovation = -1;
// grab the actual objects from mom and dad for the specified
// indexes
// if there are none, then null
if (curMom < mom.NumGenes)
{
momGene = mom.LinksChromosome[curMom];
momInnovation = momGene.InnovationId;
}
if (curDad < dad.NumGenes)
{
dadGene = dad.LinksChromosome[curDad];
dadInnovation = dadGene.InnovationId;
}
// now select a gene for mom or dad. This gene is for the baby
if ((momGene == null) && (dadGene != null))
{
if (best == dad)
{
selectedGene = dadGene;
}
curDad++;
}
else if ((dadGene == null) && (momGene != null))
{
if (best == mom)
{
selectedGene = momGene;
}
curMom++;
}
else if (momInnovation < dadInnovation)
{
if (best == mom)
{
selectedGene = momGene;
}
curMom++;
}
else if (dadInnovation < momInnovation)
{
if (best == dad)
{
selectedGene = dadGene;
}
curDad++;
}
else if (dadInnovation == momInnovation)
{
selectedGene = rnd.NextDouble() < 0.5f ? momGene : dadGene;
curMom++;
curDad++;
}
if (selectedGene != null)
{
if (selectedLinks.Count == 0)
{
selectedLinks.Add(selectedGene);
}
else
{
if (selectedLinks[selectedLinks.Count - 1]
.InnovationId != selectedGene
.InnovationId)
{
selectedLinks.Add(selectedGene);
}
}
// Check if we already have the nodes referred to in
// SelectedGene.
// If not, they need to be added.
AddNeuronId(selectedGene.FromNeuronId, selectedNeurons,
best, notBest);
AddNeuronId(selectedGene.ToNeuronId, selectedNeurons,
best, notBest);
}
}
// now create the required nodes. First sort them into order
selectedNeurons.Sort();
// finally, create the genome
var factory = (INEATGenomeFactory)_owner
.Population.GenomeFactory;
var babyGenome = factory.Factor(selectedNeurons,
selectedLinks, mom.InputCount, mom.OutputCount);
babyGenome.BirthGeneration = _owner.IterationNumber;
babyGenome.Population = _owner.Population;
babyGenome.SortGenes();
offspring[offspringIndex] = babyGenome;
}
/// <summary>
/// Called for each node in the progrmam. If this is a const node, then
/// mutate it according to the frequency and sigma specified.
/// </summary>
/// <param name="rnd">Random number generator.</param>
/// <param name="node">The node to mutate.</param>
private void MutateNode(EncogRandom rnd, ProgramNode node)
{
if (node.Template == StandardExtensions.EXTENSION_CONST_SUPPORT)
{
if (rnd.NextDouble() < _frequency)
{
ExpressionValue v = node.Data[0];
if (v.IsFloat)
{
double adj = rnd.NextGaussian()*_sigma;
node.Data[0] = new ExpressionValue(v.ToFloatValue()
+ adj);
}
}
}
foreach (ITreeNode n in node.ChildNodes)
{
var childNode = (ProgramNode) n;
MutateNode(rnd, childNode);
}
}
/// <summary>
/// Create a new genome with the specified connection density. This
/// constructor is typically used to create the initial population.
/// </summary>
/// <param name="rnd">Random number generator.</param>
/// <param name="pop">The population.</param>
/// <param name="inputCount">The input count.</param>
/// <param name="outputCount">The output count.</param>
/// <param name="connectionDensity">The connection density.</param>
public NEATGenome(EncogRandom rnd, NEATPopulation pop,
int inputCount, int outputCount,
double connectionDensity)
{
AdjustedScore = 0;
InputCount = inputCount;
OutputCount = outputCount;
// get the activation function
IActivationFunction af = pop.ActivationFunctions.PickFirst();
// first bias
int innovationId = 0;
var biasGene = new NEATNeuronGene(NEATNeuronType.Bias, af,
inputCount, innovationId++);
_neuronsList.Add(biasGene);
// then inputs
for (var i = 0; i < inputCount; i++)
{
var gene = new NEATNeuronGene(NEATNeuronType.Input, af,
i, innovationId++);
_neuronsList.Add(gene);
}
// then outputs
for (int i = 0; i < outputCount; i++)
{
var gene = new NEATNeuronGene(NEATNeuronType.Output, af,
i + inputCount + 1, innovationId++);
_neuronsList.Add(gene);
}
// and now links
for (var i = 0; i < inputCount + 1; i++)
{
for (var j = 0; j < outputCount; j++)
{
// make sure we have at least one connection
if (_linksList.Count < 1
|| rnd.NextDouble() < connectionDensity)
{
long fromId = this._neuronsList[i].Id;
long toId = this._neuronsList[inputCount + j + 1].Id;
double w = RangeRandomizer.Randomize(rnd, -pop.WeightRange, pop.WeightRange);
var gene = new NEATLinkGene(fromId, toId, true,
innovationId++, w);
_linksList.Add(gene);
}
}
}
}
/// <inheritdoc/>
public IList<NEATLinkGene> SelectLinks(EncogRandom rnd, NEATGenome genome)
{
IList<NEATLinkGene> result = new List<NEATLinkGene>();
bool mutated = false;
foreach (var linkGene in genome.LinksChromosome)
{
if (rnd.NextDouble() < _proportion)
{
mutated = true;
result.Add(linkGene);
}
}
if (!mutated)
{
int idx = rnd.Next(genome.LinksChromosome.Count);
NEATLinkGene linkGene = genome.LinksChromosome[idx];
result.Add(linkGene);
}
return result;
}
