/// <summary>
/// Sets the ModularMember (used for breeding).
/// </summary>
/// <param name="modularMember">The ModularMember to be set.</param>
private void SetModularMember(ModularMember modularMember)
{
if (!modularMember.IsGenomeAssigned)
{
throw new Exception("Genome has not been assigned to modular member!");
}
this.modularMember = modularMember;
}
/// <summary>
/// Evolves a generation of FitnessEvolvers.
/// </summary>
/// <typeparam name="T">The type of FitnessEvolvers to be evolved.</typeparam>
/// <param name="generation">The generation of FitnessEvolvers.</param>
/// <param name="selectionPercentage">The percentage of the fittest within the generation that will be selected for breeding in the next generation.</param>
/// <param name="mutationRate">The mutation rate to be used while breeding the next generation.</param>
/// <param name="fittestMember">The fittest member of the evaluated generation.</param>
/// <param name="random">The random to be used for breeding and selection.</param>
/// <param name="multithreaded">Whether the evaluation processes should happen on multiple threads.</param>
/// <returns>Returns the evolved generation.</returns>
public static T[] EvolveGeneration <T>(T[] generation, double selectionPercentage, double mutationRate, out MemberEvaluation <T, D> fittestMember, Random random, bool multithreaded = false) where T : FitnessEvolver <D>, new()
{
//Evaluate members
MemberEvaluation <T, D>[] memberEvaluations = new MemberEvaluation <T, D> [generation.Length];
if (multithreaded)
{
Parallel.For(0, generation.Length, (i) => { memberEvaluations[i] = new MemberEvaluation <T, D>(generation[i], generation[i].DetermineFitness()); });
}
else
{
for (int i = 0; i < generation.Length; i++)
{
memberEvaluations[i] = new MemberEvaluation <T, D>(generation[i], generation[i].DetermineFitness());
}
}
//Sort members
Array.Sort(memberEvaluations, delegate(MemberEvaluation <T, D> x, MemberEvaluation <T, D> y) { return(y.Fitness.CompareTo(x.Fitness)); });
//Select top
int numToSelect = (int)Math.Ceiling(selectionPercentage * generation.Length);
MemberEvaluation <T, D>[] selectedMembers = new MemberEvaluation <T, D> [numToSelect];
for (int i = 0; i < numToSelect; i++)
{
selectedMembers[i] = memberEvaluations[i];
}
fittestMember = memberEvaluations[0];
//Breed remaining
List <T> newGeneration = new List <T>(generation.Length);
for (int i = 0; i < selectedMembers.Length; i++)
{
newGeneration.Add(selectedMembers[i].FitnessMember);
}
while (newGeneration.Count < generation.Length)
{
T parent1 = WeightedSelect(selectedMembers, random);
T parent2 = WeightedSelect(selectedMembers, random);
D parent1Data = parent1.GetData();
D parent2Data = parent2.GetData();
ModularMember[] childGenomes = ModularMember.BreedMembers(parent1.ModularMember, parent2.ModularMember, mutationRate, random);
for (int i = 0; i < childGenomes.Length && newGeneration.Count < generation.Length; i++)
{
T child = new T();
child.SetModularMember(childGenomes[i]);
child.SetData(parent1Data, parent2Data);
newGeneration.Add(child);
}
}
return(newGeneration.ToArray());
}
/// <summary>
/// Breeds two ModularMembers and produces two children.
/// </summary>
/// <param name="parent1">The first parent.</param>
/// <param name="parent2">The second parent.</param>
/// <param name="mutationRate">The mutation rate during breeding.</param>
/// <param name="random">The random to be used while breeding.</param>
/// <returns>Returns the two children.</returns>
public static ModularMember[] BreedMembers(ModularMember parent1, ModularMember parent2, double mutationRate, Random random)
{
//Ensure breedable
if (parent1.CanBreedWith(parent2))
{
GeneticSequence[] child1Genome = new GeneticSequence[parent1.genome.Length];
GeneticSequence[] child2Genome = new GeneticSequence[parent1.genome.Length];
for (int i = 0; i < parent1.genome.Length; i++)
{
GeneticSequence[] crossoverSequences = GeneticSequence.Crossover(parent1.genome[i], parent2.genome[i], random);
child1Genome[i] = crossoverSequences[0];
child2Genome[i] = crossoverSequences[1];
}
//Mutate each genome
for (int i = 0; i < child1Genome.Length; i++)
{
child1Genome[i] = child1Genome[i].BitwiseMutate(mutationRate, random);
}
for (int i = 0; i < child2Genome.Length; i++)
{
child2Genome[i] = child2Genome[i].BitwiseMutate(mutationRate, random);
}
ModularMember[] parents = new ModularMember[] { parent1, parent2 };
GeneticSequence[][] childSequences = new GeneticSequence[][] { child1Genome, child2Genome };
ModularMember[] children = new ModularMember[2];
for (int i = 0; i < children.Length; i++)
{
Phenotype[] phenotypes = new Phenotype[parents[i].phenotypes.Length];
for (int j = 0; j < phenotypes.Length; j++)
{
phenotypes[j] = parents[i].phenotypes[j].Clone(parents[(i + 1) % parents.Length].phenotypes[j]);
}
children[i] = new ModularMember(phenotypes);
children[i].AssignGenome(childSequences[i]);
}
return(children);
}
else
{
throw new Exception("Provided parents are not breedable.");
}
}
/// <summary>
/// Determines if two ModularMembers can breed.
/// </summary>
/// <param name="member">The other ModularMember.</param>
/// <returns>Returns whether the ModularMembers are breedable.</returns>
public bool CanBreedWith(ModularMember member)
{
//Compare lengths
if (phenotypes.Length != member.phenotypes.Length || genome.Length != member.genome.Length || genome[0].BinarySequence.Length != member.genome[0].BinarySequence.Length)
{
return(false);
}
else
{
//Compare phenotypes
for (int i = 0; i < phenotypes.Length; i++)
{
if (!phenotypes[i].Equals(member.phenotypes[i]))
{
return(false);
}
}
return(true);
}
}
/// <summary>
/// Creates a fitness evolver with a specified ModularMember.
/// </summary>
/// <param name="modularMember">The ModularMember to be used.</param>
public FitnessEvolver(ModularMember modularMember)
{
SetModularMember(modularMember);
}