public bool IsSmallerThan(LinkGene linkGene)
{
return(this.InnovationID < linkGene.InnovationID);
}
/// <summary>
/// and a neuron. This is used in the structural mutation of NEAT
/// </summary>
/// <param name="MutationRate"></param>
/// <param name="innovation"></param>
/// <param name="NumTrysToFindOldLink"></param>
public void AddNeuron(double MutationRate, Innovation innovation, int NumTrysToFindOldLink)
{
//just return dependent on mutation rate
if (RandomProvider.RandomFloat() > MutationRate)
{
return;
}
//if a valid link is found into which to insert the new neuron
//this value is set to true.
bool bDone = false;
//this will hold the index into m_vecLinks of the chosen link gene
int ChosenLink = 0;
//first a link is chosen to split. If the genome is small the code makes
//sure one of the older links is split to ensure a chaining effect does
//not occur. Here, if the genome contains less than 5 hidden neurons it
//is considered to be too small to select a link at random
int SizeThreshold = this.NumberOfInputs + this.NumberOfOutputs + 5;
if (this.Links.Count < SizeThreshold)
{
while (NumTrysToFindOldLink-- > 0)
{
//choose a link with a bias towards the older links in the genome
ChosenLink = RandomProvider.RND.Next(0, GetNumGenes() - 1 - (int)Mathf.Sqrt(GetNumGenes()));
//make sure the link is enabled and that it is not a recurrent link
//or has a bias input
int FromNeuron = this.Links[ChosenLink].FromNeuron;
if ((this.Links[ChosenLink].Enabled) &&
(!this.Links[ChosenLink].Recurrent) &&
(this.Neurons[GetElementPosition(FromNeuron)].NeuronType != NeuronType.Bias))
{
bDone = true;
NumTrysToFindOldLink = 0;
}
}
if (!bDone)
{
//failed to find a decent link
return;
}
}
/*
* Early on in the development of the networks, a problem can occur where the same
* link is split repeatedly creating a chaining effect.
*
* Obviously, this is undesirable, so the following code checks the number of neurons
* in the genome to see if the structure is below a certain size threshold. If it is, measures
* are taken to ensure that older links are selected in preference to newer ones.
*/
else
{
int maxExitCounter = this.Links.Count + NeuralNetworkParams.NumTicks;
int exitCounter = 0;
//the genome is of sufficient size for any link to be acceptable
// 20.10.2015 also there is an exit counter to exit the loop if nothing is found in a certain amount of time. This can happen with very low ticks, the neural network gets to messed up. Reason unknown at the moment
while (!bDone && exitCounter < maxExitCounter)
{
ChosenLink = RandomProvider.RND.Next(0, GetNumGenes() - 1);
//make sure the link is enabled and that it is not a recurrent link
//or has a BIAS input
int FromNeuron = this.Links[ChosenLink].FromNeuron;
if ((this.Links[ChosenLink].Enabled) &&
(!this.Links[ChosenLink].Recurrent) &&
(this.Neurons[GetElementPosition(FromNeuron)].NeuronType != NeuronType.Bias))
{
bDone = true;
}
exitCounter++;
}
if (exitCounter >= maxExitCounter)
{
Debug.Log("No proper links where found!");
}
}
//disable this gene
this.Links[ChosenLink].Enabled = false;
//grab the weight from the gene (we want to use this for the weight of
//one of the new links so that the split does not disturb anything the
//NN may have already learned...
double OriginalWeight = this.Links[ChosenLink].Weight;
/*
* When a link is disabled and two new links are created, the old weight from the
* disabled link is used as the weight for one of the new links, and the weight for the
* other link is set to 1. In this way, the addition of a neuron creates as little disruption
* as possible to any existing learned behavior
*/
//identify the neurons this link connects
int from = this.Links[ChosenLink].FromNeuron;
int to = this.Links[ChosenLink].ToNeuron;
//calculate the depth and width of the new neuron. We can use the depth
//to see if the link feeds backwards or forwards
double NewDepth = (this.Neurons[GetElementPosition(from)].SplitY +
this.Neurons[GetElementPosition(to)].SplitY) / 2;
double NewWidth = (this.Neurons[GetElementPosition(from)].SplitX +
this.Neurons[GetElementPosition(to)].SplitX) / 2;
//Now to see if this innovation has been created previously by
//another member of the population
int id = innovation.CheckInnovation(from,
to,
InnovationType.NewNeuron);
/*it is possible for NEAT to repeatedly do the following:
*
* 1. Find a link. Lets say we choose link 1 to 5
* 2. Disable the link,
* 3. Add a new neuron and two new links
* 4. The link disabled in Step 2 maybe re-enabled when this genome
* is recombined with a genome that has that link enabled.
* 5 etc etc
*
* Therefore, this function must check to see if a neuron ID is already
* being used. If it is then the function creates a new innovation
* for the neuron. */
if (id >= 0)
{
int NeuronID = innovation.GetNeuronID(id);
if (AlreadyHaveThisNeuronID(NeuronID))
{
id = -1;
}
}
/*
* AlreadyHaveThisNeuronID returns true if (you guessed it) the genome already has a
* neuron with an identical ID. If this is the case, then a new innovation needs to be
* created, so id is reset to -1.
*/
if (id < 0)
{
//add the innovation for the new neuron
int NewNeuronID = innovation.CreateNewInnovation(from,
to,
InnovationType.NewNeuron,
NeuronType.Hidden,
NewWidth,
NewDepth);
//create the new neuron gene and add it.
this.Neurons.Add(new NeuronGene(NeuronType.Hidden,
NewNeuronID,
NewDepth,
NewWidth));
//Two new link innovations are required, one for each of the
//new links created when this gene is split.
//-----------------------------------first link
//get the next innovation ID
int idLink1 = innovation.NextNumber();
//create the new innovation
innovation.CreateNewInnovation(from,
NewNeuronID,
InnovationType.NewLink);
//create the new link gene
LinkGene link1 = new LinkGene(from,
NewNeuronID,
true,
idLink1,
1.0);
this.Links.Add(link1);
//-----------------------------------second link
//get the next innovation ID
int idLink2 = innovation.NextNumber();
//create the new innovation
innovation.CreateNewInnovation(NewNeuronID,
to,
InnovationType.NewLink);
//create the new gene
LinkGene link2 = new LinkGene(NewNeuronID,
to,
true,
idLink2,
OriginalWeight);
this.Links.Add(link2);
}
else
{
//this innovation has already been created so grab the relevant neuron
//and link info from the innovation database
int NewNeuronID = innovation.GetNeuronID(id);
//get the innovation IDs for the two new link genes.
int idLink1 = innovation.CheckInnovation(from, NewNeuronID, InnovationType.NewLink);
int idLink2 = innovation.CheckInnovation(NewNeuronID, to, InnovationType.NewLink);
//this should never happen because the innovations *should* have already
//occurred
if ((idLink1 < 0) || (idLink2 < 0))
{
Debug.Log("Error in CGenome::AddNeuron, Problem!");
return;
}
//now we need to create 2 new genes to represent the new links
LinkGene link1 = new LinkGene(from, NewNeuronID, true, idLink1, 1.0);
LinkGene link2 = new LinkGene(NewNeuronID, to, true, idLink2, OriginalWeight);
this.Links.Add(link1);
this.Links.Add(link2);
//create the new neuron
NeuronGene NewNeuron = new NeuronGene(NeuronType.Hidden, NewNeuronID, NewDepth, NewWidth);
//and add it
this.Neurons.Add(NewNeuron);
}
return;
}
Genome Crossover(Genome mum, Genome dad)
{
// TODO: Test the Crossover in Unity
//helps make the code clearer
//first, calculate the genome we will using the disjoint/excess
//genes from. This is the fittest genome.
parent_type best;
//if they are of equal fitness use the shorter (because we want to keep
//the networks as small as possible)
if (mum.GetFitnessValue() == dad.GetFitnessValue())
{
//if they are of equal fitness and length just choose one at
//random
if (mum.GetNumGenes() == dad.GetNumGenes())
{
best = (parent_type)RandomProvider.RND.Next(0, 1);
}
else
{
if (mum.GetNumGenes() < dad.GetNumGenes())
{
best = parent_type.MUM;
}
else
{
best = parent_type.DAD;
}
}
}
else
{
if (mum.GetFitnessValue() > dad.GetFitnessValue())
{
best = parent_type.MUM;
}
else
{
best = parent_type.DAD;
}
}
//these vectors will hold the offspring's nodes and genes
List <NeuronGene> BabyNeurons = new List <NeuronGene>();
List <LinkGene> BabyGenes = new List <LinkGene>();
//temporary List to store all added node IDs
List <int> vecNeurons = new List <int>();
//create iterators so we can step through each parents genes and set
//them to the first gene of each parent
LinkGene curMum = mum.GetStartOfGenes();
LinkGene curDad = dad.GetStartOfGenes();
//int curMumIndex = 0;
//int curDadIndex = 0;
//this will hold a copy of the gene we wish to add at each step
LinkGene SelectedGene = null;
int mumActualItemCount = mum.Links.Count - 1;
int dadActualItemCount = dad.Links.Count - 1;
//step through each parents genes until we reach the end of both
//while (!((curMumIndex > mumActualItemCount) && (curDadIndex > dadActualItemCount)))
for (int curMumIndex = 0, curDadIndex = 0; curMumIndex < mum.Links.Count && curDadIndex < dad.Links.Count;)
{
if (curDadIndex > dadActualItemCount)
{
curDadIndex = dadActualItemCount;
}
if (curMumIndex > mumActualItemCount)
{
curMumIndex = mumActualItemCount;
}
curMum = mum.Links[curMumIndex];
curDad = dad.Links[curDadIndex];
//the end of mum's genes have been reached
if (((curMumIndex >= mumActualItemCount) && (curDadIndex != dadActualItemCount)))
{
//if dad is fittest
if (best == parent_type.DAD)
{
//add dads genes
SelectedGene = curDad;
}
//move onto dad's next gene
++curDadIndex;
}
//the end of dads's genes have been reached
else if ((curDadIndex >= dadActualItemCount) && (curMumIndex != mumActualItemCount))
{
//if mum is fittest
if (best == parent_type.MUM)
{
//add mums genes
SelectedGene = curMum;
}
//move onto mum's next gene
++curMumIndex;
}
//if mums innovation number is less than dads
else if (curMum.InnovationID < curDad.InnovationID)
{
//if mum is fittest add gene
if (best == parent_type.MUM)
{
SelectedGene = curMum;
}
//move onto mum's next gene
++curMumIndex;
}
//if dads innovation number is less than mums
else if (curDad.InnovationID < curMum.InnovationID)
{
//if dad is fittest add gene
if (best == parent_type.DAD)
{
SelectedGene = curDad;
}
//move onto dad's next gene
++curDadIndex;
}
//if innovation numbers are the same
else if (curDad.InnovationID == curMum.InnovationID)
{
//grab a gene from either parent
if (RandomProvider.RandomFloat() < 0.5f)
{
SelectedGene = curMum;
}
else
{
SelectedGene = curDad;
}
//move onto next gene of each parent
++curMumIndex;
++curDadIndex;
}
if (SelectedGene == null)
{
int x = 0;
continue;
}
//add the selected gene if not already added
if (BabyGenes.Count == 0)
{
BabyGenes.Add(SelectedGene);
}
else
{
if (BabyGenes[BabyGenes.Count - 1].InnovationID !=
SelectedGene.InnovationID)
{
BabyGenes.Add(SelectedGene);
}
}
//Check if we already have the nodes referred to in SelectedGene.
//If not, they need to be added.
AddNeuronID(SelectedGene.FromNeuron, vecNeurons);
AddNeuronID(SelectedGene.ToNeuron, vecNeurons);
if (vecNeurons.Count == 13 && !vecNeurons.Contains(11))
{
int yyy = 0;
}
}//end while
//now create the required nodes. First sort them into order
vecNeurons.Sort(delegate(int x, int y)
{
return(x.CompareTo(y));
});
for (int i = 0; i < vecNeurons.Count; i++)
{
BabyNeurons.Add(Innovation.CreateNeuronFromID(vecNeurons[i]));
}
//finally, create the genome
Genome babyGenome = new Genome(NextGenomeID++,
BabyNeurons,
BabyGenes,
mum.GetNumInputs(),
mum.GetNumOutputs());
return(babyGenome);
}
/// <summary>
/// Add a link to the genome dependent upon the mutation rate. This is used in the structural mutation of NEAT
/// </summary>
/// <param name="MutationRate"></param>
/// <param name="ChanceOfRecurrent"></param>
/// <param name="innovation"></param>
/// <param name="NumTrysToFindLoop"></param>
/// <param name="NumTrysToAddLink"></param>
public void AddLink(double MutationRate, double ChanceOfRecurrent, Innovation innovation, int NumTrysToFindLoop, int NumTrysToAddLink)
{
//just return dependent on the mutation rate
if (RandomProvider.RandomFloat() > MutationRate)
{
return;
}
//define holders for the two neurons to be linked. If we have find two
//valid neurons to link these values will become >= 0.
int ID_neuron1 = -1;
int ID_neuron2 = -1;
//flag set if a recurrent link is selected (looped or normal)
bool bRecurrent = false;
//first test to see if an attempt shpould be made to create a
//link that loops back into the same neuron
if (RandomProvider.RandomFloat() < ChanceOfRecurrent)
{
//YES: try NumTrysToFindLoop times to find a neuron that is not an
//input or bias neuron and that does not already have a loopback
//connection
while (NumTrysToFindLoop-- > 0)
{
//grab a random neuron
int NeuronPos = RandomProvider.RND.Next(this.NumberOfInputs + 1, this.Neurons.Count - 1);
//check to make sure the neuron does not already have a loopback
//link and that it is not an input or bias neuron
if (!this.Neurons[NeuronPos].Recurrent &&
(this.Neurons[NeuronPos].NeuronType != NeuronType.Bias) &&
(this.Neurons[NeuronPos].NeuronType != NeuronType.Input))
{
ID_neuron1 = ID_neuron2 = this.Neurons[NeuronPos].ID;
this.Neurons[NeuronPos].Recurrent = true;
bRecurrent = true;
NumTrysToFindLoop = 0;
}
}
}
/*
* First, the code checks to see if there is a chance of a looped recurrent link being
* added. If so, then it attempts NumTrysToFindLoop times to find an appropriate neuron. If
* no neuron is found, the program continues to look for two unconnected neurons.
*/
else
{
//No: try to find two unlinked neurons. Make NumTrysToAddLink
//attempts
/*
* Because some networks will already have existing connections between all its available
* neurons, the code has to make sure it doesn’t enter an infinite loop when it
* tries to find two unconnected neurons. To prevent this from happening, the program
* only tries NumTrysToAddLink times to find two unlinked neurons.
*/
while (NumTrysToAddLink-- > 0)
{
//choose two neurons, the second must not be an input or a bias
ID_neuron1 = this.Neurons[RandomProvider.RND.Next(0, this.Neurons.Count - 1)].ID;
ID_neuron2 =
this.Neurons[RandomProvider.RND.Next(this.NumberOfInputs + 1, this.Neurons.Count - 1)].ID;
if (ID_neuron2 == 2)
{
continue;
}
//make sure these two are not already linked and that they are
//not the same neuron
if (!(DuplicateLink(ID_neuron1, ID_neuron2) ||
(ID_neuron1 == ID_neuron2)))
{
NumTrysToAddLink = 0;
}
else
{
ID_neuron1 = -1;
ID_neuron2 = -1;
}
}
}
//return if unsuccessful in finding a link
if ((ID_neuron1 < 0) || (ID_neuron2 < 0))
{
return;
}
//check to see if we have already created this innovation
/*
* Here, the code examines the innovation database to see if this link has already been
* discovered by another genome. CheckInnovation returns either the ID number of the
* innovation or, if the link is a new innovation, a negative value.
*/
int id = innovation.CheckInnovation(ID_neuron1, ID_neuron2, InnovationType.NewLink);
//is this link recurrent?
if (this.Neurons[GetElementPosition(ID_neuron1)].SplitY >
this.Neurons[GetElementPosition(ID_neuron2)].SplitY)
{
bRecurrent = true;
}
/*
* Here, the split values for the two neurons are compared to see if the link feeds
* forward or backward.
*/
if (id < 0)
{
//we need to create a new innovation
innovation.CreateNewInnovation(ID_neuron1, ID_neuron2, InnovationType.NewLink);
//then create the new gene
id = innovation.NextNumber() - 1;
/*
* If the program enters this section of code, then the innovation is a new one. Before
* the new gene is created, the innovation is added to the database and an identification
* number is retrieved. The new gene will be tagged with this identification
* number.
*/
LinkGene NewGene = new LinkGene(ID_neuron1,
ID_neuron2,
true,
id,
RandomProvider.RandomClamped(),
bRecurrent);
this.Links.Add(NewGene);
}
else
{
//the innovation has already been created so all we need to
//do is create the new gene using the existing innovation ID
LinkGene NewGene = new LinkGene(ID_neuron1,
ID_neuron2,
true,
id,
RandomProvider.RandomClamped(),
bRecurrent);
this.Links.Add(NewGene);
}
return;
}
