public override IGenome CreateOffspring_Sexual(EvolutionAlgorithm ea, IGenome parent)
{
NeatGenome otherParent = parent as NeatGenome;
if (otherParent == null)
return null;
// Build a list of connections in either this genome or the other parent.
CorrelationResults correlationResults = CorrelateConnectionGeneLists(connectionGeneList, otherParent.connectionGeneList);
Debug.Assert(correlationResults.PerformIntegrityCheck(), "CorrelationResults failed integrity check.");
//----- Connection Genes.
// We will temporarily store the offspring's genes in newConnectionGeneList and keeping track of which genes
// exist with newConnectionGeneTable. Here we ensure these objects are created, and if they already existed
// then ensure they are cleared. Clearing existing objects is more efficient that creating new ones because
// allocated memory can be re-used.
// Key = connection key, value = index in newConnectionGeneList.
if(newConnectionGeneTable==null)
{ // Provide a capacity figure to the new Hashtable. The offspring will be the same length (or thereabouts).
newConnectionGeneTable = new Hashtable(connectionGeneList.Count);
}
else
{
newConnectionGeneTable.Clear();
}
//TODO: No 'capacity' constructor on CollectionBase. Create modified/custom CollectionBase.
// newConnectionGeneList must be constructed on each call because it is passed to a new NeatGenome
// at construction time and a permanent reference to the list is kept.
newConnectionGeneList = new ConnectionGeneList(ConnectionGeneList.Count);
// A switch that stores which parent is fittest 1 or 2. Chooses randomly if both are equal. More efficient to calculate this just once.
byte fitSwitch;
if(Fitness > otherParent.Fitness)
fitSwitch = 1;
else if(Fitness < otherParent.Fitness)
fitSwitch = 2;
else
{ // Select one of the parents at random to be the 'master' genome during crossover.
if(Utilities.NextDouble() < 0.5)
fitSwitch = 1;
else
fitSwitch = 2;
}
bool combineDisjointExcessFlag = Utilities.NextDouble() < ea.NeatParameters.pDisjointExcessGenesRecombined;
// Loop through the correlationResults, building a table of ConnectionGenes from the parents that will make it into our
// new [single] offspring. We use a table keyed on connection end points to prevent passing connections to the offspring
// that may have the same end points but a different innovation number - effectively we filter out duplicate connections.
int idxBound = correlationResults.CorrelationItemList.Count;
for(int i=0; i<idxBound; i++)
{
CreateOffspring_Sexual_ProcessCorrelationItem((CorrelationItem)correlationResults.CorrelationItemList[i], fitSwitch, combineDisjointExcessFlag, ea.NeatParameters);
}
//----- Neuron Genes.
// Build a neuronGeneList by analysing each connection's neuron end-point IDs.
// This strategy has the benefit of eliminating neurons that are no longer connected too.
// Remember to always keep all input, output and bias neurons though!
NeuronGeneList newNeuronGeneList = new NeuronGeneList(neuronGeneList.Count);
// Keep a table of the NeuronGene ID's keyed by ID so that we can keep track of which ones have been added.
// Key = innovation ID, value = null for some reason.
if(newNeuronGeneTable==null)
newNeuronGeneTable = new Hashtable(neuronGeneList.Count);
else
newNeuronGeneTable.Clear();
// Get the input/output neurons from this parent. All Genomes share these neurons, they do not change during a run.
idxBound = neuronGeneList.Count;
for(int i=0; i<idxBound; i++)
{
if(neuronGeneList[i].NeuronType != NeuronType.Hidden)
{
newNeuronGeneList.Add(new NeuronGene(neuronGeneList[i]));
newNeuronGeneTable.Add(neuronGeneList[i].InnovationId, null);
}
else
{ // No more bias, input or output nodes. break the loop.
break;
}
}
// Now analyse the connections to determine which NeuronGenes are required in the offspring.
// Loop through every connection in the child, and add to the child those hidden neurons that are sources or targets of the connection.
idxBound = newConnectionGeneList.Count;
for(int i=0; i<idxBound; i++)
{
NeuronGene neuronGene;
ConnectionGene connectionGene = newConnectionGeneList[i];
if(!newNeuronGeneTable.ContainsKey(connectionGene.SourceNeuronId))
{
//TODO: DAVID proper activation function
// We can safely assume that any missing NeuronGenes at this point are hidden heurons.
neuronGene = this.neuronGeneList.GetNeuronById(connectionGene.SourceNeuronId);
if (neuronGene != null)
newNeuronGeneList.Add(new NeuronGene(neuronGene));
else
newNeuronGeneList.Add(new NeuronGene(otherParent.NeuronGeneList.GetNeuronById(connectionGene.SourceNeuronId)));
//newNeuronGeneList.Add(new NeuronGene(connectionGene.SourceNeuronId, NeuronType.Hidden, ActivationFunctionFactory.GetActivationFunction("SteepenedSigmoid")));
newNeuronGeneTable.Add(connectionGene.SourceNeuronId, null);
}
if(!newNeuronGeneTable.ContainsKey(connectionGene.TargetNeuronId))
{
//TODO: DAVID proper activation function
// We can safely assume that any missing NeuronGenes at this point are hidden heurons.
neuronGene = this.neuronGeneList.GetNeuronById(connectionGene.TargetNeuronId);
if (neuronGene != null)
newNeuronGeneList.Add(new NeuronGene(neuronGene));
else
newNeuronGeneList.Add(new NeuronGene(otherParent.NeuronGeneList.GetNeuronById(connectionGene.TargetNeuronId)));
//newNeuronGeneList.Add(new NeuronGene(connectionGene.TargetNeuronId, NeuronType.Hidden, ActivationFunctionFactory.GetActivationFunction("SteepenedSigmoid")));
newNeuronGeneTable.Add(connectionGene.TargetNeuronId, null);
}
}
// Determine which modules to pass on to the child in the same way.
// For each module in this genome or in the other parent, if it was referenced by even one connection add it and all its dummy neurons to the child.
List<ModuleGene> newModuleGeneList = new List<ModuleGene>();
// Build a list of modules the child might have, which is a union of the parents' module lists, but they are all copies so we can't just do a union.
List<ModuleGene> unionParentModules = new List<ModuleGene>(moduleGeneList);
foreach (ModuleGene moduleGene in otherParent.moduleGeneList) {
bool alreadySeen = false;
foreach (ModuleGene match in unionParentModules) {
if (moduleGene.InnovationId == match.InnovationId) {
alreadySeen = true;
break;
}
}
if (!alreadySeen) {
unionParentModules.Add(moduleGene);
}
}
foreach (ModuleGene moduleGene in unionParentModules) {
// Examine each neuron in the child to determine whether it is part of a module.
foreach (List<uint> dummyNeuronList in new List<uint>[] { moduleGene.InputIds, moduleGene.OutputIds }) {
foreach (uint dummyNeuronId in dummyNeuronList) {
if (newNeuronGeneTable.ContainsKey(dummyNeuronId)) {
goto childHasModule;
}
}
}
continue; // the child does not contain this module, so continue the loop and check for the next module.
childHasModule: // the child does contain this module, so make sure the child gets all the nodes the module requires to work.
// Make sure the child has all the neurons in the given module.
newModuleGeneList.Add(new ModuleGene(moduleGene));
foreach (List<uint> dummyNeuronList in new List<uint>[] { moduleGene.InputIds, moduleGene.OutputIds }) {
foreach (uint dummyNeuronId in dummyNeuronList) {
if (!newNeuronGeneTable.ContainsKey(dummyNeuronId)) {
newNeuronGeneTable.Add(dummyNeuronId, null);
NeuronGene neuronGene = this.neuronGeneList.GetNeuronById(dummyNeuronId);
if (neuronGene != null) {
newNeuronGeneList.Add(new NeuronGene(neuronGene));
} else {
newNeuronGeneList.Add(new NeuronGene(otherParent.NeuronGeneList.GetNeuronById(dummyNeuronId)));
}
}
}
}
}
// TODO: Inefficient code?
newNeuronGeneList.SortByInnovationId();
// Schrum: Need to calculate this because of Module Mutation adding extra outputs
int revisedOutputCount = 0;
foreach(NeuronGene n in newNeuronGeneList) {
if (n.NeuronType == NeuronType.Output)
revisedOutputCount++;
}
// newConnectionGeneList is already sorted because it was generated by passing over the list returned by
// CorrelateConnectionGeneLists() - which is always in order.
// Schrum: Modified to add outputsPerPolicy as a parameter, and use revisedOutputCount
return new NeatGenome(ea.NextGenomeId, newNeuronGeneList, newModuleGeneList, newConnectionGeneList, inputNeuronCount, revisedOutputCount, outputsPerPolicy);
}
public override IGenome CreateOffspring_Sexual(EvolutionAlgorithm ea, IGenome parent)
{
CorrelationResults correlationResults = CorrelateConnectionGeneLists(connectionGeneList, ((NeatGenome)parent).connectionGeneList);
Debug.Assert(correlationResults.PerformIntegrityCheck(), "CorrelationResults failed integrity check.");
//----- Connection Genes.
// We will temporarily store the offspring's genes in newConnectionGeneList and keeping track of which genes
// exist with newConnectionGeneTable. Here we ensure these objects are created, and if they already existed
// then ensure they are cleared. Clearing existing objects is more efficient that creating new ones because
// allocated memory can be re-used.
if(newConnectionGeneTable==null)
{ // Provide a capacity figure to the new Hashtable. The offspring will be the same length (or thereabouts).
newConnectionGeneTable = new Hashtable(connectionGeneList.Count);
}
else
{
newConnectionGeneTable.Clear();
}
//TODO: No 'capacity' constructor on CollectionBase. Create modified/custom CollectionBase.
// newConnectionGeneList must be constructed on each call because it is passed to a new NeatGenome
// at construction time and a permanent reference to the list is kept.
newConnectionGeneList = new ConnectionGeneList(ConnectionGeneList.Count);
// A switch that stores which parent is fittest 1 or 2. 0 if both are equal. More efficient to calculate this just once.
byte fitSwitch;
if(Fitness > parent.Fitness)
fitSwitch = 1;
else if(Fitness < parent.Fitness)
fitSwitch = 2;
else
{ // Select one of the parents at random to be the 'master' genome during crossover.
if(Utilities.NextDouble() < 0.5)
fitSwitch = 1;
else
fitSwitch = 2;
}
bool combineDisjointExcessFlag = Utilities.NextDouble() < ea.NeatParameters.pDisjointExcessGenesRecombined ? true : false;
// Loop through the correlationResults, building a table of ConnectionGenes from the parent's that will make it into our
// new [single] offspring. We use a table keyed on connection end points to prevent passing connections to the offspring
// that may have the same end points but a different innovation number - effectively we filter out duplicate connections.
int idxBound = correlationResults.CorrelationItemList.Count;
for(int i=0; i<idxBound; i++)
{
CreateOffspring_Sexual_ProcessCorrelationItem((CorrelationItem)correlationResults.CorrelationItemList[i], fitSwitch, combineDisjointExcessFlag, ea.NeatParameters);
}
//----- Neuron Genes.
// Build a neuronGeneList by analysing each connection's neuron end-point IDs.
// This strategy has the benefit of eliminating neurons that are no longer connected too.
// Remember to always keep all input, output and bias neurons though!
NeuronGeneList newNeuronGeneList = new NeuronGeneList(neuronGeneList.Count);
// Keep a table of the NeuronGene ID's keyed by ID so that we can keep track of which ones have been added.
if(newNeuronGeneTable==null)
newNeuronGeneTable = new Hashtable(neuronGeneList.Count);
else
newNeuronGeneTable.Clear();
// Get the input/output neurons from this parent. All Genomes share these neurons, they do not change during a run.
idxBound = neuronGeneList.Count;
for(int i=0; i<idxBound; i++)
{
if(neuronGeneList[i].NeuronType != NeuronType.Hidden)
{
newNeuronGeneList.Add(new NeuronGene(neuronGeneList[i]));
newNeuronGeneTable.Add(neuronGeneList[i].InnovationId, null);
}
else
{ // No more bias, input or output nodes. break the loop.
break;
}
}
// Now analyse the connections to determine which NeuronGenes are required in the offspring.
idxBound = newConnectionGeneList.Count;
for(int i=0; i<idxBound; i++)
{
NeuronGene neuronGene;
ConnectionGene connectionGene = newConnectionGeneList[i];
if(!newNeuronGeneTable.ContainsKey(connectionGene.SourceNeuronId))
{
//TODO: DAVID proper activation function
// We can safely assume that any missing NeuronGenes at this point are hidden heurons.
neuronGene = this.neuronGeneList.GetNeuronById(connectionGene.SourceNeuronId);
if (neuronGene != null)
newNeuronGeneList.Add(new NeuronGene(neuronGene));
else
newNeuronGeneList.Add(new NeuronGene(((NeatGenome)parent).NeuronGeneList.GetNeuronById(connectionGene.SourceNeuronId)));
//newNeuronGeneList.Add(new NeuronGene(connectionGene.SourceNeuronId, NeuronType.Hidden, ActivationFunctionFactory.GetActivationFunction("SteepenedSigmoid")));
newNeuronGeneTable.Add(connectionGene.SourceNeuronId, null);
}
if(!newNeuronGeneTable.ContainsKey(connectionGene.TargetNeuronId))
{
//TODO: DAVID proper activation function
// We can safely assume that any missing NeuronGenes at this point are hidden heurons.
neuronGene = this.neuronGeneList.GetNeuronById(connectionGene.TargetNeuronId);
if (neuronGene != null)
newNeuronGeneList.Add(new NeuronGene(neuronGene));
else
newNeuronGeneList.Add(new NeuronGene(((NeatGenome)parent).NeuronGeneList.GetNeuronById(connectionGene.TargetNeuronId)));
//newNeuronGeneList.Add(new NeuronGene(connectionGene.TargetNeuronId, NeuronType.Hidden, ActivationFunctionFactory.GetActivationFunction("SteepenedSigmoid")));
newNeuronGeneTable.Add(connectionGene.TargetNeuronId, null);
}
}
// TODO: Inefficient code?
newNeuronGeneList.SortByInnovationId();
// newConnectionGeneList is already sorted because it was generated by passing over the list returned by
// CorrelateConnectionGeneLists() - which is always in order.
return new NeatGenome(ea.NextGenomeId, newNeuronGeneList, newConnectionGeneList, inputNeuronCount, outputNeuronCount);
}