/// <summary>
/// Crosses over this genome with the given genome, giving both the same score. Gives the created genome this genome's random.
/// </summary>
/// <param name="genome">The genome to cross over with.</param>
/// <returns>The crossed-over genome.</returns>
/// <exception cref="ArgumentNullException">When the given genome is null.</exception>
public Genome Crossover(Genome genome)
{
return(Crossover(0, genome, 0, Random));
}
/// <summary>
/// Gets the distance between this genome and the given genome. Higher = less compatible.
/// </summary>
/// <param name="genome">The genome to compare to.</param>
/// <returns>The distance between this genome and the given genome.</returns>
/// <remarks>
/// The distance d can be measured by the following equation:
/// <para/>
/// d = c1(E / N) + c2(D / N) + c3 * W
/// <para/>
/// Where:
/// d = distance |
/// E = # excess genes |
/// D = # of disjoint genes |
/// W = weight difference of similar genes |
/// N = # of genes in largest genome (this or them), 1 if #genes < 20 |
/// c_ = constant for adjusting
/// </remarks>
/// <exception cref="ArgumentNullException">When the given genome is null.</exception>
public double Distance(Genome genome)
{
Helpers.ThrowOnNull(genome, "genome");
int index_me = 0; //Leaving indexes from old implementation due to their mathematical usefulness later.
int index_them = 0;
int num_excess = 0; //The number of excess genes.
int num_disjoint = 0; //The number of disjoint genes.
double num_similar = 0; //The number of genes that are similar.
double weight_diff = 0; //The weight difference between similar genes.
SortedDictionary <int, ConnectionGene> .ValueCollection.Enumerator enumerator_me = ConnectionGenes.Values.GetEnumerator();
SortedDictionary <int, ConnectionGene> .ValueCollection.Enumerator enumerator_them = genome.ConnectionGenes.Values.GetEnumerator();
enumerator_me.MoveNext(); //Preps for first current.
enumerator_them.MoveNext();
//Step through both genomes and find out how different they are.
//This method is run a lot, so we want to do this by innovation number for efficiency.
while (index_me < ConnectionGenes.Count && index_them < genome.ConnectionGenes.Count)
{
ConnectionGene connectionGene_me = enumerator_me.Current;
ConnectionGene connectionGene_them = enumerator_them.Current;
int inNum_me = connectionGene_me.ConnectionGenePattern.InnovationNumber;
int inNum_them = connectionGene_them.ConnectionGenePattern.InnovationNumber;
if (inNum_me == inNum_them) //Similar genes.
{
++index_me;
++index_them;
enumerator_me.MoveNext();
enumerator_them.MoveNext();
++num_similar;
weight_diff += Math.Abs(connectionGene_me.Weight - connectionGene_them.Weight);
}
else if (inNum_me > inNum_them) //Disjoint gene at them, increase them.
{
++index_them;
enumerator_them.MoveNext();
++num_disjoint;
}
else //Disjoint gene at me, increase me.
{
++index_me;
enumerator_me.MoveNext();
++num_disjoint;
}
}
//Count excess genes.
if (index_me < ConnectionGenes.Count) //We have leftover genes, use our count.
{
num_excess = ConnectionGenes.Count - index_me;
}
else if (index_them < genome.ConnectionGenes.Count) //They have leftover genes, use their count.
{
num_excess = genome.ConnectionGenes.Count - index_them;
}
//There is no else because if they have the same number of genes, there is no excess, so use the default 0.
if (num_similar > 0)
{
//The weight_diff would be 0 if num_similar was too, so only do this if they aren't 0.
weight_diff /= num_similar;
}
double N = Math.Max(ConnectionGenes.Count, genome.ConnectionGenes.Count);
N = (N < 20) ? 1 : N; //Only needed for large networks.
return(Pedigree.C1 * (num_excess / N) + Pedigree.C2 * (num_disjoint / N) + (Pedigree.C3 * weight_diff));
}
/// <summary>
/// Crosses over this genome with the given genome.
/// </summary>
/// <param name="my_score">Score of this genome. See remarks.</param>
/// <param name="genome">The genome to cross over with.</param>
/// <param name="their_score">Score of the given genome. See remarks.</param>
/// <param name="random">The random object for the created genome.</param>
/// <returns>The crossed-over genome.</returns>
/// <remarks>
/// When the given scores are equal, use the following rules:
/// <list type="bullet">
/// <item>On similar connection genes: See <see cref="NEAT.Genetic.Tracker.Pedigree.UniformCrossover"/>.</item>
/// <item>On disjoint connection genes: Adds all to the created genome.</item>
/// <item>On excess connection genes: Adds all to the created genome.</item>
/// </list>
///
/// When the given scores are not equal, use the following rules:
/// <list type="bullet">
/// <item>On similar connection genes: See <see cref="NEAT.Genetic.Tracker.Pedigree.UniformCrossover"/>.</item>
/// <item>On disjoint connection genes: Adds genes from parent with higher score.</item>
/// <item>On excess connection genes: Adds genes from parent with higher score.</item>
/// </list>
/// </remarks>
/// <exception cref="ArgumentNullException">When the genome or random is null.</exception>
public Genome Crossover(double my_score, Genome genome, double their_score, Random random)
{
Helpers.ThrowOnNull(genome, "genome");
Helpers.ThrowOnNull(random, "random");
Genome created_genome = new Genome(Pedigree, random);
#region ConnectionGenes
SortedDictionary <int, ConnectionGene> .ValueCollection.Enumerator enumerator_me = ConnectionGenes.Values.GetEnumerator();
SortedDictionary <int, ConnectionGene> .ValueCollection.Enumerator enumerator_them = genome.ConnectionGenes.Values.GetEnumerator();
enumerator_me.MoveNext(); //Preps for first current.
enumerator_them.MoveNext();
//Step through both genomes and cross them over randomly.
while (enumerator_me.Current != null && enumerator_them.Current != null)
{
ConnectionGene connectionGene_me = enumerator_me.Current;
ConnectionGene connectionGene_them = enumerator_them.Current;
int inNum_me = connectionGene_me.ConnectionGenePattern.InnovationNumber;
int inNum_them = connectionGene_them.ConnectionGenePattern.InnovationNumber;
if (inNum_me == inNum_them) //Similar genes, choose either side at random.
{
enumerator_me.MoveNext();
enumerator_them.MoveNext();
if (Pedigree.UniformCrossover)
{
if (random.NextDouble() < .5)
{
created_genome.ConnectionGenes.Add(inNum_me, Pedigree.Copy_ConnectionGene(connectionGene_me));
}
else
{
created_genome.ConnectionGenes.Add(inNum_them, Pedigree.Copy_ConnectionGene(connectionGene_them));
}
}
else
{
created_genome.ConnectionGenes.Add(inNum_me, Pedigree.Create_ConnectionGene(connectionGene_me.ConnectionGenePattern,
(connectionGene_me.Weight + connectionGene_them.Weight) / 2,
(random.NextDouble() < .5) ? connectionGene_me.Enabled : connectionGene_them.Enabled));
}
}
else if (inNum_me > inNum_them) //Disjoint gene at them, add this gene if allowed.
{
enumerator_them.MoveNext();
if (Math.Abs(my_score - their_score) < Pedigree.Crossover_ScoreDelta || their_score > my_score)
{
created_genome.ConnectionGenes.Add(inNum_them, Pedigree.Copy_ConnectionGene(connectionGene_them));
}
}
else //Disjoint gene at me, add this gene if allowed.
{
enumerator_me.MoveNext();
if (Math.Abs(my_score - their_score) < Pedigree.Crossover_ScoreDelta || my_score > their_score)
{
created_genome.ConnectionGenes.Add(inNum_me, Pedigree.Copy_ConnectionGene(connectionGene_me));
}
}
}
//Run through the excess connections and add them all if allowed.
if (enumerator_me.Current != null) //We have leftover genes, add ours if allowed.
{
if (Math.Abs(my_score - their_score) < Pedigree.Crossover_ScoreDelta || my_score > their_score) //Check legality.
{
do
{
created_genome.ConnectionGenes.Add(enumerator_me.Current.ConnectionGenePattern.InnovationNumber,
Pedigree.Copy_ConnectionGene(enumerator_me.Current));
}while (enumerator_me.MoveNext());
}
}
else if (enumerator_them.Current != null) //They have leftover genes, add theirs if allowed.
{
if (Math.Abs(my_score - their_score) < Pedigree.Crossover_ScoreDelta || their_score > my_score) //Check legality.
{
do
{
created_genome.ConnectionGenes.Add(enumerator_them.Current.ConnectionGenePattern.InnovationNumber,
Pedigree.Copy_ConnectionGene(enumerator_them.Current));
}while (enumerator_them.MoveNext());
}
}
//There is no else because if they have the same number of genes, there is no excess, so don't do anything.
#endregion ConnectionGenes
#region NodeGenes
//Input/Output nodes and bias node.
for (int i = 1; i <= Pedigree.Num_InputNodes + Pedigree.Num_OutputNodes + 1; ++i) //The +1 handles the bias node.
{
created_genome.NodeGenes.Add(i, NodeGenes[i]);
}
//Every other relavent node.
foreach (ConnectionGene connectionGene in created_genome.ConnectionGenes.Values)
{
ConnectionGenePattern pattern = connectionGene.ConnectionGenePattern;
if (!created_genome.NodeGenes.ContainsKey(pattern.From.InnovationNumber))
{
NodeGene nodeGene_toCopy;
try
{
nodeGene_toCopy = NodeGenes[pattern.From.InnovationNumber];
}
catch (KeyNotFoundException)
{
nodeGene_toCopy = genome.NodeGenes[pattern.From.InnovationNumber];
}
created_genome.NodeGenes.Add(pattern.From.InnovationNumber, Pedigree.Copy_NodeGene(nodeGene_toCopy));
}
if (!created_genome.NodeGenes.ContainsKey(pattern.To.InnovationNumber))
{
NodeGene nodeGene_toCopy;
try
{
nodeGene_toCopy = NodeGenes[pattern.To.InnovationNumber];
}
catch (KeyNotFoundException)
{
nodeGene_toCopy = genome.NodeGenes[pattern.To.InnovationNumber];
}
created_genome.NodeGenes.Add(pattern.To.InnovationNumber, Pedigree.Copy_NodeGene(nodeGene_toCopy));
}
}
#endregion NodeGenes
return(created_genome);
}