public ReproduceAndReplace_Node(ScoredDistribution <Organism> scoredDistribution_organisms, Species new_species)
{
this.scoredDistribution_organisms = scoredDistribution_organisms;
this.new_species = new_species;
}
/// <summary>
/// Performs speciation, adjusting fitness sums, culling, crossover, and mutation, to generate the next generation of Neural Networks
/// </summary>
public void Select()
{
speciesList = new List <Species>();
foreach (NeuralNetwork NN in Networks)
{
bool foundSpecies = false;
foreach (Species species in speciesList)
{
if (NeuralNetwork.EvolutionaryDistance(species.networks.ElementAt(0), NN, innovationNumbers) < SpeciatingThreshold)
{
species.networks.Add(NN);
foundSpecies = true;
break;
}
}
if (!foundSpecies)
{
speciesList.Add(new Species(NN));
}
}
//Console.WriteLine("speciesList Count after speciating: " + speciesList.Count);
//Console.WriteLine("Pop Consistency: " + isConsistent());
foreach (Species species in speciesList)
{
species.networks.Sort((NN1, NN2) => NN2.Fitness.CompareTo(NN1.Fitness));
int kill = (int)(Math.Ceiling(species.networks.Count() * killRate));
int total = species.networks.Count();
for (int i = species.networks.Count() - 1; i > total - 1 - kill; i--)
{
species.networks.RemoveAt(i);
}
for (int i = 0; i < species.networks.Count(); i++)
{
species.adjustedFitnessSum += species.networks.ElementAt(i).Fitness / (species.networks.Count);
}
}
speciesList.RemoveAll(x => x.networks.Count < 3);
//Console.WriteLine("speciesList Count after killing: " + speciesList.Count);
//Console.WriteLine("Pop Consistency: " + isConsistent());
int numSelectionBreed = (int)(.75 * Size);
speciesList.Sort((species1, species2) => species2.adjustedFitnessSum.CompareTo(species1.adjustedFitnessSum));
double sharedFitnessTotal = 0;
for (int i = 0; i < speciesList.Count / 3; i++)
{
Species species = speciesList.ElementAt(i);
sharedFitnessTotal += species.adjustedFitnessSum;
}
//Console.WriteLine("speciesList Count after adjusting fitness sums: " + speciesList.Count);
//Console.WriteLine("Pop Consistency: " + isConsistent());
List <NeuralNetwork> childrenNetworks = new List <NeuralNetwork>();
Random rand = new Random();
for (int i = 0; i < speciesList.Count / 3; i++)
{
Species species = speciesList.ElementAt(i);
for (int j = 0; j < numSelectionBreed * (species.adjustedFitnessSum / sharedFitnessTotal); j++)
{
//Console.WriteLine("Pop Consistency: " + isConsistent());
NeuralNetwork NN1 = species.networks.ElementAt(rand.Next(species.networks.Count));
NeuralNetwork NN2 = species.networks.ElementAt(rand.Next(species.networks.Count));
NeuralNetwork Child = NeuralNetwork.Crossover(NN1, NN2, innovationNumbers);
Child.RandomMutation();
//Child.DisplayOutputConnections();
childrenNetworks.Add(Child);
//Console.WriteLine("Pop Consistency: " + isConsistent());
//Console.WriteLine();
}
}
//Console.WriteLine();
//Console.WriteLine("speciesList Count after selection breeding: "+ speciesList.Count);
//Console.WriteLine("Pop Consistency: " + isConsistent());
//Console.WriteLine();
Networks.Sort((NN1, NN2) => NN2.Fitness.CompareTo(NN1.Fitness));
for (int i = 0; i < 5; i++)
{
childrenNetworks.Add(Networks.ElementAt(i));
}
int numRandomBreed = Networks.Count - childrenNetworks.Count;
for (int i = 0; i < numRandomBreed; i++)
{
Species randSpecies = speciesList[rand.Next(speciesList.Count)];
NeuralNetwork randNN1 = randSpecies.networks[rand.Next(randSpecies.networks.Count)];
NeuralNetwork randNN2 = randSpecies.networks[rand.Next(randSpecies.networks.Count)];
NeuralNetwork child = NeuralNetwork.Crossover(randNN1, randNN2, innovationNumbers);
child.RandomMutation();
//child.DisplayOutputConnections();
childrenNetworks.Add(child);
}
//Console.WriteLine();
//Console.WriteLine("speciesList Count after random breeding: " + speciesList.Count);
//Console.WriteLine("Pop Consistency: " + isConsistent());
//Console.WriteLine();
Networks = new List <NeuralNetwork>(childrenNetworks);
//Console.WriteLine("total child networks after selection: " + childrenNetworks.Count);
//Console.WriteLine("Pop Consistency: " + isConsistent());
}
/// <summary>
/// Reproduces the surviving species based on their fitness scores. Chooses the organisms in the species to mate based on fitness scores as well. Replaces the previous generation
/// with the newly created organisms. Removes extinct species if necessary. Adjusts compatibility distance after replacement.
/// See <see cref="NEAT.NEATClient.Evolve"/> before using!
/// </summary>
/// <remarks>
/// Decides the amount of offspring (nk) each species (k) should be allotted via the following equation:
/// <para/>
/// nk = P * (Fk / Ft)
/// <para/>
/// Where:
/// <list type="bullet">
/// <item>k: The species.</item>
/// <item>nk: The number of new organisms for species k in the next generation.</item>
/// <item>P: The desired new population count.</item>
/// <item>Fk: The average fitness score of the species.</item>
/// <item>Ft: The sum of all fitness score averages of every species.</item>
/// </list>
/// </remarks>
public void ReproduceAndReplace()
{
double total_speciesFitness = Species.Sum(x => x.AverageFitnessScore);
Dictionary <Species, ReproduceAndReplace_Node> stored_distributionsSpecies = new Dictionary <Species, ReproduceAndReplace_Node>(Species.Count);
List <Organism> next_generation = new List <Organism>(NumOrganisms); //All of the new organisms.
HashSet <Species> next_species = new HashSet <Species>(Species.Count);
#region Reproduction
foreach (Species species in Species)
{
//Calculate the number of organisms every species will have:
int num_alloted_offspring = (int)(NumOrganisms * (species.AverageFitnessScore / total_speciesFitness));
//Prepare new species for placement:
Species creation_species = new Species();
next_species.Add(creation_species);
//Prepare ScoredDistribution for selecting organisms to mate:
ScoredDistribution <Organism> internal_organisms = new ScoredDistribution <Organism>(species.Size, Pedigree.Random);
foreach (Organism organism in species)
{
internal_organisms.Add(organism, organism.FitnessScore);
}
stored_distributionsSpecies.Add(species, new ReproduceAndReplace_Node(internal_organisms, creation_species));
//Actaully mate the organisms:
for (int i = 0; i < num_alloted_offspring; ++i)
{
Organism random_organism_1 = internal_organisms.ChooseValue();
Organism random_organism_2 = internal_organisms.ChooseValue(random_organism_1); //We don't want any self-replication...
if (random_organism_2 == null)
{
random_organism_2 = random_organism_1; //Unless that's the only option ;) Only happens when the organism is the only one in the species.
}
Organism baby = new Organism(random_organism_1.Genome.Crossover(random_organism_1.FitnessScore, random_organism_2.Genome, random_organism_2.FitnessScore,
Pedigree.Random));
next_generation.Add(baby);
creation_species.AddOrganism(baby);
}
}
while (next_generation.Count < NumOrganisms) //We need more organisms, give it to some random species. Super rare that this doesn't happen.
{
ReproduceAndReplace_Node chosen_last_node = stored_distributionsSpecies[Species.RandomValue(Pedigree.Random)];
ScoredDistribution <Organism> last_round_distribution = chosen_last_node.scoredDistribution_organisms;
Species last_round_species = chosen_last_node.new_species;
Organism random_organism_1 = last_round_distribution.ChooseValue();
Organism random_organism_2 = last_round_distribution.ChooseValue(random_organism_1); //We don't want any self-replication...
if (random_organism_2 == null)
{
random_organism_2 = random_organism_1; //Unless that's the only option ;) Only happens when the organism is the only one in the species.
}
Organism baby = new Organism(random_organism_1.Genome.Crossover(random_organism_1.FitnessScore, random_organism_2.Genome, random_organism_2.FitnessScore,
Pedigree.Random));
next_generation.Add(baby);
last_round_species.AddOrganism(baby);
}
#endregion Reproduction
#region Replacement
//Organisms:
Organisms.Clear();
Organisms.AddRange(next_generation);
//Species:
Species.Clear();
foreach (Species species in next_species)
{
Species.Add(species);
}
#endregion Replacement
RemoveExtinctions();
CompatibilityDistance += CD_function.Invoke(Species.Count);
}
/// <summary>
/// Removes all stored extinct species (species with no organisms). See <see cref="NEAT.NEATClient.Evolve"/> before using!
/// </summary>
public void RemoveExtinctions()
{
Species.RemoveWhere(x => x.Size == 0);
}
/// <summary>
/// Separates the current organisms into the correct species. Makes new species and deletes extinct species as needed. See <see cref="NEAT.NEATClient.Evolve"/> before using!
/// </summary>
public void Speciate()
{
foreach (Organism organism in Organisms)
{
bool found_species = false;
foreach (Species species in Species)
{
Organism random_organism = species.GetRandomOrganism(Pedigree.Random, organism);
if (random_organism == null) //The organism we're at is the only organism in the species we're at. See if we should leave it in this species.
{
bool kill_species = false;
IEnumerable <Species> all_other_species = Species.Where(x => x != species);
foreach (Species other_species in all_other_species)
{
Organism other_random_organism = other_species.GetRandomOrganism(Pedigree.Random); //No need to exclude the organism because it can't be here.
if (organism.Genome.Distance(other_random_organism.Genome) < CompatibilityDistance) //The organism can fit in another species, put it there.
{
organism.Species.RemoveOrganism(organism);
other_species.AddOrganism(organism);
kill_species = true;
break;
}
}
if (kill_species) //This species no longer has any organisms, it is now extinct.
{
Species.Remove(species);
}
found_species = true;
break;
}
if (organism.Genome.Distance(random_organism.Genome) < CompatibilityDistance)
{
if (organism.Species != random_organism.Species) //If the species to set is actually different.
{
Species original_species = organism.Species;
original_species?.RemoveOrganism(organism);
species.AddOrganism(organism);
if (original_species?.Size == 0) //This species just went extinct.
{
Species.Remove(original_species);
}
}
found_species = true;
break;
}
}
if (!found_species)
{
Species original_species = organism.Species;
original_species?.RemoveOrganism(organism);
Species new_species = new Species();
Species.Add(new_species);
new_species.AddOrganism(organism);
if (original_species?.Size == 0) //This species just went extinct.
{
Species.Remove(original_species);
}
}
}
}
/// <summary>
/// Breed
/// </summary>
private void Breed()
{
// Logs Setup
int weightMutationNb = 0;
int addConnectionNb = 0;
int addNodeNb = 0;
int enableDisableConnectionNb = 0;
_mutationLogs = "";
while (_nextEvaluationGenomes.Count() < _config.populationSize)
{
Genome child = null;
Genome mom = null;
Genome dad = null;
// Get Child
Species species = null;
int maxToKeep = ((_config.populationSize * _config.percentageToKeep) / 100) - 1;
if (_config.species)
{
species = GetRandomSpecies(_r);
}
if (_config.crossover)
{
if (species != null)
{
mom = GetRandomGenome(species, _r);
dad = GetRandomGenome(species, _r);
}
else
{
mom = _genomes[UnityEngine.Random.Range(0, maxToKeep)];
dad = _genomes[UnityEngine.Random.Range(0, maxToKeep)];
}
// Crossover between Mom & Dad
child = mom.Fitness >= dad.Fitness ? Genome.Crossover(mom, dad, _r, _config.disabledConnectionInheritChance) : Genome.Crossover(dad, mom, _r, _config.disabledConnectionInheritChance);
}
else
{
child = species != null ? new Genome(GetRandomGenome(species, _r)) : new Genome(_genomes[UnityEngine.Random.Range(0, maxToKeep)]);
}
// Weights Mutation
if ((float)_r.NextDouble() < _config.mutationRate)
{
child.WeightsMutation(_r);
weightMutationNb++;
}
if (_config.genomeMutations)
{
// Add Connection Mutation
if ((float)_r.NextDouble() < _config.addConnectionRate)
{
// If for Logs
if (child.AddConnectionMutation(_r, _connectionInnovation, 10))
{
addConnectionNb++;
}
}
// Add Node Mutation
if ((float)_r.NextDouble() < _config.addNodeRate)
{
child.AddNodeMutation(_r, _connectionInnovation, _nodeInnovation);
addNodeNb++;
}
// Enable/Disable a Random Connection
if ((float)_r.NextDouble() < _config.enableDisableRate)
{
// If for Logs
if (child.EnableOrDisableRandomConnection())
{
enableDisableConnectionNb++;
}
}
}
// Add Child to Next Evaluation Genomes
_nextEvaluationGenomes.Add(child);
}
_mutationLogs += string.Format(
"Weights Mutation: {0}, Add Connection: {1}, Add Node: {2}, Enable/Disable Connection: {3}\nCrossover is {4}, Genome Mutations is {5}, Species is {6}",
weightMutationNb,
addConnectionNb,
addNodeNb,
enableDisableConnectionNb,
_config.crossover,
_config.genomeMutations,
_config.species
);
_genomes.Clear();
_genomes = new List <Genome>(_nextEvaluationGenomes);
_nextEvaluationGenomes = new List <Genome>();
}
/// <summary>
/// Eliminate the species with its genomes.
/// </summary>
private void EliminateSpecies(Species speciesTarget)
{
Genomes.RemoveAll(x => speciesTarget.Genomes.Contains(x));
SpeciesCtrl.SpeciesList.Remove(speciesTarget);
}
