/// <summary>
/// Determines if a neuron is still needed. If all links to/from a neuron
/// have been removed, then the neuron is no longer needed.
/// </summary>
/// <param name="target">The target genome.</param>
/// <param name="neuronID">The neuron id to check for.</param>
/// <returns>Returns true, if the neuron is still needed.</returns>
public bool IsNeuronNeeded(NEATGenome target, long neuronID)
{
// do not remove bias or input neurons or output
foreach (NEATNeuronGene gene in target.NeuronsChromosome)
{
if (gene.Id == neuronID)
{
NEATNeuronGene neuron = gene;
if ((neuron.NeuronType == NEATNeuronType.Input) ||
(neuron.NeuronType == NEATNeuronType.Bias) ||
(neuron.NeuronType == NEATNeuronType.Output))
{
return(true);
}
}
}
// Now check to see if the neuron is used in any links
foreach (NEATLinkGene gene in target.LinksChromosome)
{
NEATLinkGene linkGene = gene;
if (linkGene.FromNeuronId == neuronID)
{
return(true);
}
if (linkGene.ToNeuronId == neuronID)
{
return(true);
}
}
return(false);
}
/// <inheritdoc/>
public override void PerformOperation(EncogRandom rnd, IGenome[] parents,
int parentIndex, IGenome[] offspring,
int offspringIndex)
{
var target = ObtainGenome(parents, parentIndex, offspring,
offspringIndex);
if (target.LinksChromosome.Count < MinLink)
{
// don't remove from small genomes
return;
}
// determine the target and remove
var index = RangeRandomizer.RandomInt(0, target
.LinksChromosome.Count - 1);
NEATLinkGene targetGene = target.LinksChromosome[index];
target.LinksChromosome.Remove(targetGene);
// if this orphaned any nodes, then kill them too!
if (!IsNeuronNeeded(target, targetGene.FromNeuronId))
{
RemoveNeuron(target, targetGene.FromNeuronId);
}
if (!IsNeuronNeeded(target, targetGene.ToNeuronId))
{
RemoveNeuron(target, targetGene.ToNeuronId);
}
}
/// <summary>
/// Create a link between two neuron id's. Create or find any necessary
/// innovation records.
/// </summary>
/// <param name="target">The target genome.</param>
/// <param name="neuron1Id">The id of the source neuron.</param>
/// <param name="neuron2Id">The id of the target neuron.</param>
/// <param name="weight">The weight of this new link.</param>
public void CreateLink(NEATGenome target, long neuron1Id,
long neuron2Id, double weight)
{
// first, does this link exist? (and if so, hopefully disabled,
// otherwise we have a problem)
foreach (NEATLinkGene linkGene in target.LinksChromosome)
{
if ((linkGene.FromNeuronId == neuron1Id) &&
(linkGene.ToNeuronId == neuron2Id))
{
// bring the link back, at the new weight
linkGene.Enabled = true;
linkGene.Weight = weight;
return;
}
}
// check to see if this innovation has already been tried
NEATInnovation innovation = ((NEATPopulation)target
.Population).Innovations.FindInnovation(neuron1Id,
neuron2Id);
// now create this link
var lg = new NEATLinkGene(neuron1Id, neuron2Id,
true, innovation.InnovationId, weight);
target.LinksChromosome.Add(lg);
}
/// <inheritdoc/>
public void MutateWeight(EncogRandom rnd, NEATLinkGene linkGene,
double weightRange)
{
double delta = rnd.NextGaussian() * _sigma;
double w = linkGene.Weight + delta;
w = NEATPopulation.ClampWeight(w, weightRange);
linkGene.Weight = w;
}
/// <inheritdoc/>
public IMLMethod Decode(IGenome genome)
{
var neatGenome = (NEATGenome)genome;
var pop = (NEATPopulation)neatGenome.Population;
IList <NEATNeuronGene> neuronsChromosome = neatGenome.NeuronsChromosome;
IList <NEATLinkGene> linksChromosome = neatGenome.LinksChromosome;
if (neuronsChromosome[0].NeuronType != NEATNeuronType.Bias)
{
throw new NeuralNetworkError(
"The first neuron must be the bias neuron, this genome is invalid.");
}
var links = new List <NEATLink>();
var afs = new IActivationFunction[neuronsChromosome.Count];
for (int i = 0; i < afs.Length; i++)
{
afs[i] = neuronsChromosome[i].ActivationFunction;
}
IDictionary <long, int> lookup = new Dictionary <long, int>();
for (int i = 0; i < neuronsChromosome.Count; i++)
{
NEATNeuronGene neuronGene = neuronsChromosome[i];
lookup[neuronGene.Id] = i;
}
// loop over connections
for (int i = 0; i < linksChromosome.Count; i++)
{
NEATLinkGene linkGene = linksChromosome[i];
if (linkGene.Enabled)
{
links.Add(new NEATLink(lookup[linkGene.FromNeuronId],
lookup[linkGene.ToNeuronId], linkGene.Weight));
}
}
links.Sort();
NEATNetwork network = new NEATNetwork(neatGenome.InputCount,
neatGenome.OutputCount, links, afs);
network.ActivationCycles = pop.ActivationCycles;
return(network);
}
/// <inheritdoc/>
public IList <NEATLinkGene> SelectLinks(EncogRandom rnd,
NEATGenome genome)
{
IList <NEATLinkGene> result = new List <NEATLinkGene>();
int cnt = Math.Min(_linkCount, genome.LinksChromosome.Count);
while (result.Count < cnt)
{
int idx = rnd.Next(genome.LinksChromosome.Count);
NEATLinkGene link = genome.LinksChromosome[idx];
if (!result.Contains(link))
{
result.Add(link);
}
}
return(result);
}
/// <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);
}
/// <inheritdoc/>
public override void PerformOperation(EncogRandom rnd, IGenome[] parents,
int parentIndex, IGenome[] offspring,
int offspringIndex)
{
var target = ObtainGenome(parents, parentIndex, offspring,
offspringIndex);
var countTrysToFindOldLink = Owner.MaxTries;
var pop = ((NEATPopulation)target.Population);
// the link to split
NEATLinkGene splitLink = null;
int sizeBias = ((NEATGenome)parents[0]).InputCount
+ ((NEATGenome)parents[0]).OutputCount + 10;
// if there are not at least
int upperLimit;
if (target.LinksChromosome.Count < sizeBias)
{
upperLimit = target.NumGenes - 1
- (int)Math.Sqrt(target.NumGenes);
}
else
{
upperLimit = target.NumGenes - 1;
}
while ((countTrysToFindOldLink--) > 0)
{
// choose a link, use the square root to prefer the older links
int i = RangeRandomizer.RandomInt(0, upperLimit);
NEATLinkGene link = target.LinksChromosome[i];
// get the from neuron
long fromNeuron = link.FromNeuronId;
if ((link.Enabled) &&
(target.NeuronsChromosome
[GetElementPos(target, fromNeuron)]
.NeuronType != NEATNeuronType.Bias))
{
splitLink = link;
break;
}
}
if (splitLink == null)
{
return;
}
splitLink.Enabled = false;
long from = splitLink.FromNeuronId;
long to = splitLink.ToNeuronId;
NEATInnovation innovation = ((NEATPopulation)Owner.Population).Innovations
.FindInnovationSplit(from, to);
// add the splitting neuron
IActivationFunction af = ((NEATPopulation)Owner.Population).ActivationFunctions.Pick(new Random());
target.NeuronsChromosome.Add(
new NEATNeuronGene(NEATNeuronType.Hidden, af, innovation
.NeuronId, innovation.InnovationId));
// add the other two sides of the link
CreateLink(target, from, innovation.NeuronId,
splitLink.Weight);
CreateLink(target, innovation.NeuronId, to, pop.WeightRange);
target.SortGenes();
}
/// <summary>
/// Read the object.
/// </summary>
/// <param name="mask0">The stream to read the object from.</param>
/// <returns>The object that was loaded.</returns>
public virtual Object Read(Stream mask0)
{
var result = new NEATPopulation();
var innovationList = new NEATInnovationList {
Population = result
};
result.Innovations = innovationList;
var ins0 = new EncogReadHelper(mask0);
IDictionary <Int32, ISpecies> speciesMap = new Dictionary <Int32, ISpecies>();
IDictionary <ISpecies, Int32> leaderMap = new Dictionary <ISpecies, Int32>();
IDictionary <Int32, IGenome> genomeMap = new Dictionary <Int32, IGenome>();
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("INNOVATIONS"))
{
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
var innovation = new NEATInnovation
{
InnovationID = Int32.Parse(cols[0]),
InnovationType = StringToInnovationType(cols[1]),
NeuronType = StringToNeuronType(cols[2]),
SplitX = CSVFormat.EgFormat.Parse(cols[3]),
SplitY = CSVFormat.EgFormat.Parse(cols[4]),
NeuronID = Int32.Parse(cols[5]),
FromNeuronID = Int32.Parse(cols[6]),
ToNeuronID = Int32.Parse(cols[7])
};
result.Innovations.Add(innovation);
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("SPECIES"))
{
foreach (String line in section.Lines)
{
String[] cols = line.Split(',');
var species = new BasicSpecies
{
SpeciesID = Int32.Parse(cols[0]),
Age = Int32.Parse(cols[1]),
BestScore = CSVFormat.EgFormat.Parse(cols[2]),
GensNoImprovement = Int32.Parse(cols[3]),
SpawnsRequired = CSVFormat.EgFormat
.Parse(cols[4])
};
species.SpawnsRequired = CSVFormat.EgFormat
.Parse(cols[5]);
leaderMap[(species)] = (Int32.Parse(cols[6]));
result.Species.Add(species);
speciesMap[((int)species.SpeciesID)] = (species);
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("GENOMES"))
{
NEATGenome lastGenome = null;
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
if (cols[0].Equals("g", StringComparison.InvariantCultureIgnoreCase))
{
lastGenome = new NEATGenome
{
NeuronsChromosome = new Chromosome(),
LinksChromosome = new Chromosome()
};
lastGenome.Chromosomes.Add(lastGenome.NeuronsChromosome);
lastGenome.Chromosomes.Add(lastGenome.LinksChromosome);
lastGenome.GenomeID = Int32.Parse(cols[1]);
lastGenome.SpeciesID = Int32.Parse(cols[2]);
lastGenome.AdjustedScore = CSVFormat.EgFormat
.Parse(cols[3]);
lastGenome.AmountToSpawn = CSVFormat.EgFormat
.Parse(cols[4]);
lastGenome.NetworkDepth = Int32.Parse(cols[5]);
lastGenome.Score = CSVFormat.EgFormat.Parse(cols[6]);
result.Add(lastGenome);
genomeMap[(int)lastGenome.GenomeID] = lastGenome;
}
else if (cols[0].Equals("n", StringComparison.InvariantCultureIgnoreCase))
{
var neuronGene = new NEATNeuronGene
{
Id = Int32.Parse(cols[1]),
NeuronType = StringToNeuronType(cols[2]),
Enabled = Int32.Parse(cols[3]) > 0,
InnovationId = Int32.Parse(cols[4]),
ActivationResponse = CSVFormat.EgFormat
.Parse(cols[5]),
SplitX = CSVFormat.EgFormat.Parse(cols[6]),
SplitY = CSVFormat.EgFormat.Parse(cols[7])
};
lastGenome.Neurons.Add(neuronGene);
}
else if (cols[0].Equals("l", StringComparison.InvariantCultureIgnoreCase))
{
var linkGene = new NEATLinkGene();
linkGene.Id = Int32.Parse(cols[1]);
linkGene.Enabled = Int32.Parse(cols[2]) > 0;
linkGene.Recurrent = Int32.Parse(cols[3]) > 0;
linkGene.FromNeuronID = Int32.Parse(cols[4]);
linkGene.ToNeuronID = Int32.Parse(cols[5]);
linkGene.Weight = CSVFormat.EgFormat.Parse(cols[6]);
linkGene.InnovationId = Int32.Parse(cols[7]);
lastGenome.Links.Add(linkGene);
}
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("CONFIG"))
{
IDictionary <String, String> paras = section.ParseParams();
result.NeatActivationFunction = EncogFileSection
.ParseActivationFunction(paras,
NEATPopulation.PropertyNEATActivation);
result.OutputActivationFunction = EncogFileSection
.ParseActivationFunction(paras,
NEATPopulation.PropertyOutputActivation);
result.Snapshot = EncogFileSection.ParseBoolean(paras,
PersistConst.Snapshot);
result.InputCount = EncogFileSection.ParseInt(paras,
PersistConst.InputCount);
result.OutputCount = EncogFileSection.ParseInt(paras,
PersistConst.OutputCount);
result.OldAgePenalty = EncogFileSection.ParseDouble(paras,
PopulationConst.PropertyOldAgePenalty);
result.OldAgeThreshold = EncogFileSection.ParseInt(paras,
PopulationConst.PropertyOldAgeThreshold);
result.PopulationSize = EncogFileSection.ParseInt(paras,
PopulationConst.PropertyPopulationSize);
result.SurvivalRate = EncogFileSection.ParseDouble(paras,
PopulationConst.PropertySurvivalRate);
result.YoungBonusAgeThreshhold = EncogFileSection.ParseInt(
paras, PopulationConst.PropertyYoungAgeThreshold);
result.YoungScoreBonus = EncogFileSection.ParseDouble(paras,
PopulationConst.PropertyYoungAgeBonus);
result.GenomeIDGenerate.CurrentID = EncogFileSection.ParseInt(paras,
PopulationConst.
PropertyNextGenomeID);
result.InnovationIDGenerate.CurrentID = EncogFileSection.ParseInt(paras,
PopulationConst.
PropertyNextInnovationID);
result.GeneIDGenerate.CurrentID = EncogFileSection.ParseInt(paras,
PopulationConst.
PropertyNextGeneID);
result.SpeciesIDGenerate.CurrentID = EncogFileSection.ParseInt(paras,
PopulationConst.
PropertyNextSpeciesID);
}
}
// now link everything up
// first put all the genomes into correct species
foreach (IGenome genome in result.Genomes)
{
var neatGenome = (NEATGenome)genome;
var speciesId = (int)neatGenome.SpeciesID;
if (speciesMap.ContainsKey(speciesId))
{
ISpecies s = speciesMap[speciesId];
s.Members.Add(neatGenome);
}
neatGenome.InputCount = result.InputCount;
neatGenome.OutputCount = result.OutputCount;
}
// set the species leader links
foreach (ISpecies species in leaderMap.Keys)
{
int leaderID = leaderMap[species];
IGenome leader = genomeMap[leaderID];
species.Leader = leader;
((BasicSpecies)species).Population = result;
}
return(result);
}
/// <summary>
/// Create a link between two neuron id's. Create or find any necessary
/// innovation records.
/// </summary>
/// <param name="target">The target genome.</param>
/// <param name="neuron1Id">The id of the source neuron.</param>
/// <param name="neuron2Id">The id of the target neuron.</param>
/// <param name="weight">The weight of this new link.</param>
public void CreateLink(NEATGenome target, long neuron1Id,
long neuron2Id, double weight)
{
// first, does this link exist? (and if so, hopefully disabled,
// otherwise we have a problem)
foreach (NEATLinkGene linkGene in target.LinksChromosome)
{
if ((linkGene.FromNeuronId == neuron1Id)
&& (linkGene.ToNeuronId == neuron2Id))
{
// bring the link back, at the new weight
linkGene.Enabled = true;
linkGene.Weight = weight;
return;
}
}
// check to see if this innovation has already been tried
NEATInnovation innovation = ((NEATPopulation)target
.Population).Innovations.FindInnovation(neuron1Id,
neuron2Id);
// now create this link
var lg = new NEATLinkGene(neuron1Id, neuron2Id,
true, innovation.InnovationId, weight);
target.LinksChromosome.Add(lg);
}
/// <inheritdoc/>
public void MutateWeight(EncogRandom rnd, NEATLinkGene linkGene,
double weightRange)
{
double delta = rnd.NextGaussian() * _sigma;
double w = linkGene.Weight + delta;
w = NEATPopulation.ClampWeight(w, weightRange);
linkGene.Weight = w;
}
/// <inheritdoc/>
public void MutateWeight(EncogRandom rnd, NEATLinkGene linkGene, double weightRange)
{
linkGene.Weight = RangeRandomizer.Randomize(rnd, -weightRange,
weightRange);
}
/// <inheritdoc/>
public Object Read(Stream istream)
{
long nextInnovationId = 0;
long nextGeneId = 0;
var result = new NEATPopulation();
var innovationList = new NEATInnovationList {
Population = result
};
result.Innovations = innovationList;
var reader = new EncogReadHelper(istream);
EncogFileSection section;
while ((section = reader.ReadNextSection()) != null)
{
if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("INNOVATIONS"))
{
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection
.SplitColumns(line);
var innovation = new NEATInnovation();
var innovationId = int.Parse(cols[1]);
innovation.InnovationId = innovationId;
innovation.NeuronId = int.Parse(cols[2]);
result.Innovations.Innovations[cols[0]] = innovation;
nextInnovationId = Math.Max(nextInnovationId, innovationId + 1);
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("SPECIES"))
{
NEATGenome lastGenome = null;
BasicSpecies lastSpecies = null;
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
if (String.Compare(cols[0], "s", StringComparison.OrdinalIgnoreCase) == 0)
{
lastSpecies = new BasicSpecies
{
Population = result,
Age = int.Parse(cols[1]),
BestScore = CSVFormat.EgFormat.Parse(cols[2]),
GensNoImprovement = int.Parse(cols[3])
};
result.Species.Add(lastSpecies);
}
else if (String.Compare(cols[0], "g", StringComparison.OrdinalIgnoreCase) == 0)
{
bool isLeader = lastGenome == null;
lastGenome = new NEATGenome
{
InputCount = result.InputCount,
OutputCount = result.OutputCount,
Species = lastSpecies,
AdjustedScore = CSVFormat.EgFormat.Parse(cols[1]),
Score = CSVFormat.EgFormat.Parse(cols[2]),
BirthGeneration = int.Parse(cols[3])
};
lastSpecies.Add(lastGenome);
if (isLeader)
{
lastSpecies.Leader = lastGenome;
}
}
else if (String.Compare(cols[0], "n", StringComparison.OrdinalIgnoreCase) == 0)
{
var neuronGene = new NEATNeuronGene();
int geneId = int.Parse(cols[1]);
neuronGene.Id = geneId;
IActivationFunction af = EncogFileSection.ParseActivationFunction(cols[2]);
neuronGene.ActivationFunction = af;
neuronGene.NeuronType = PersistNEATPopulation.StringToNeuronType(cols[3]);
neuronGene.InnovationId = int.Parse(cols[4]);
lastGenome.NeuronsChromosome.Add(neuronGene);
nextGeneId = Math.Max(geneId + 1, nextGeneId);
}
else if (String.Compare(cols[0], "l", StringComparison.OrdinalIgnoreCase) == 0)
{
var linkGene = new NEATLinkGene
{
Id = int.Parse(cols[1]),
Enabled = (int.Parse(cols[2]) > 0),
FromNeuronId = int.Parse(cols[3]),
ToNeuronId = int.Parse(cols[4]),
Weight = CSVFormat.EgFormat.Parse(cols[5]),
InnovationId = int.Parse(cols[6])
};
lastGenome.LinksChromosome.Add(linkGene);
}
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("CONFIG"))
{
IDictionary <string, string> prm = section.ParseParams();
string afStr = prm[NEATPopulation.PropertyNEATActivation];
if (String.Compare(afStr, TypeCppn, StringComparison.OrdinalIgnoreCase) == 0)
{
HyperNEATGenome.BuildCPPNActivationFunctions(result.ActivationFunctions);
}
else
{
result.NEATActivationFunction = EncogFileSection.ParseActivationFunction(prm,
NEATPopulation.PropertyNEATActivation);
}
result.ActivationCycles = EncogFileSection.ParseInt(prm,
PersistConst.ActivationCycles);
result.InputCount = EncogFileSection.ParseInt(prm,
PersistConst.InputCount);
result.OutputCount = EncogFileSection.ParseInt(prm,
PersistConst.OutputCount);
result.PopulationSize = EncogFileSection.ParseInt(prm,
NEATPopulation.PropertyPopulationSize);
result.SurvivalRate = EncogFileSection.ParseDouble(prm,
NEATPopulation.PropertySurvivalRate);
result.ActivationCycles = EncogFileSection.ParseInt(prm,
NEATPopulation.PropertyCycles);
}
}
// set factories
if (result.IsHyperNEAT)
{
result.GenomeFactory = new FactorHyperNEATGenome();
result.CODEC = new HyperNEATCODEC();
}
else
{
result.GenomeFactory = new FactorNEATGenome();
result.CODEC = new NEATCODEC();
}
// set the next ID's
result.InnovationIDGenerate.CurrentID = nextInnovationId;
result.GeneIdGenerate.CurrentID = nextGeneId;
// find first genome, which should be the best genome
if (result.Species.Count > 0)
{
ISpecies species = result.Species[0];
if (species.Members.Count > 0)
{
result.BestGenome = species.Members[0];
}
}
return(result);
}
/// <inheritdoc/>
public void MutateWeight(EncogRandom rnd, NEATLinkGene linkGene, double weightRange)
{
linkGene.Weight = RangeRandomizer.Randomize(rnd, -weightRange,
weightRange);
}
/// <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;
}