public Chromosome ToChromosome()
{
Chromosome chromosome = new Chromosome();
for (int j = 0; j < data.Count; ++j)
{
// if not black edge
if (Math.Abs(data[j].Item1 - data[j].Item2) != 1)
{
continue;
}
if (data[j].Item1 % 2 == data[j].Item2 % 2)
{
continue;
}
if (data[j].Item1 < data[j].Item2)
{
chromosome.Add(data[j].Item2 / 2);
}
else
{
chromosome.Add(-data[j].Item1 / 2);
}
}
return(chromosome);
}
/// <summary>
/// Construct a genome, do not provide links and neurons.
/// </summary>
///
/// <param name="id">The genome id.</param>
/// <param name="inputCount_0">The input count.</param>
/// <param name="outputCount_1">The output count.</param>
public NEATGenome(long id, int inputCount_0, int outputCount_1)
{
GenomeID = id;
AdjustedScore = 0;
inputCount = inputCount_0;
outputCount = outputCount_1;
AmountToSpawn = 0;
speciesID = 0;
double inputRowSlice = 0.8d / (inputCount_0);
neuronsChromosome = new Chromosome();
linksChromosome = new Chromosome();
Chromosomes.Add(neuronsChromosome);
Chromosomes.Add(linksChromosome);
for (int i = 0; i < inputCount_0; i++)
{
neuronsChromosome.Add(new NEATNeuronGene(NEATNeuronType.Input,
i, 0, 0.1d + i * inputRowSlice));
}
neuronsChromosome.Add(new NEATNeuronGene(NEATNeuronType.Bias,
inputCount_0, 0, 0.9d));
double outputRowSlice = 1 / (double)(outputCount_1 + 1);
for (int i_2 = 0; i_2 < outputCount_1; i_2++)
{
neuronsChromosome.Add(new NEATNeuronGene(
NEATNeuronType.Output, i_2 + inputCount_0 + 1, 1, (i_2 + 1)
* outputRowSlice));
}
for (int i_3 = 0; i_3 < inputCount_0 + 1; i_3++)
{
for (int j = 0; j < outputCount_1; j++)
{
linksChromosome.Add(new NEATLinkGene(
((NEATNeuronGene)neuronsChromosome.Get(i_3)).Id,
((NEATNeuronGene)Neurons.Get(
inputCount_0 + j + 1)).Id, true, inputCount_0
+ outputCount_1 + 1 + NumGenes,
RangeRandomizer.Randomize(-1, 1), false));
}
}
}
private void addToChildAndSwap(Chromosome child, List <Gene> chosenGeneSet, GraphNode chosenNode, List <Gene> notChosenGeneSet, int choiceIndex)
{
child.Add(chosenGeneSet[choiceIndex]);
var swapIndex = notChosenGeneSet.FindIndex((g) => ((GraphNode)g.ObjectValue).GetHashCode() == chosenNode.GetHashCode());
swapElements(notChosenGeneSet, choiceIndex, swapIndex);
}
private void addToChildAndRemoveFromParents(Chromosome child, List <Gene> chosenGeneSet, GraphNode chosenNode, List <Gene> notChosenGeneSet)
{
child.Add(chosenGeneSet[0]);
chosenGeneSet.RemoveAt(0);
var removeIndex = notChosenGeneSet.FindIndex((g) => ((GraphNode)g.ObjectValue).GetHashCode() == chosenNode.GetHashCode());
notChosenGeneSet.RemoveAt(0);
}
/*
* CycleToChromosome(Nodes)
* for j ← 1 to |Nodes|/2
* if Node2j−1 < Node2j
* Chromosomej ← Node2j /2
* else
* Chromosomej ← −Node2j−1/2
* return Chromosome
*/
public Chromosome ToChromosome()
{
Chromosome chromosome = new Chromosome();
for (int j = 0; j < Count / 2; ++j)
{
if (this[2 * j] < this[2 * j + 1])
{
chromosome.Add(this[2 * j + 1] / 2);
}
else
{
chromosome.Add(-this[2 * j] / 2);
}
}
return(chromosome);
}
static void Main(string[] args)
{
// Initialize the game table, matches and distances
WorldCupChronogram.Initialize();
Distances.Initialize();
// Population, with a chromossome length
var population = new Population(CHROMOSOME_SIZE);
// Random seed
var random = new Random();
// Initialize the population
for (int i = 0; i < INITIAL_POPULATION_SIZE; i++)
{
var chromossome = new Chromosome();
for (int j = 0; j < CHROMOSOME_SIZE; j++)
{
// Somedays have more or less games
var maxGamesInDays = WorldCupChronogram.Days[j].NumberOfGames;
var gene = new Gene(random.Next(maxGamesInDays));
chromossome.Add(gene);
}
population.Solutions.Add(chromossome);
}
// Elite operator
var elite = new Elite(ETILISM);
// Crossover operator
var crossover = new Crossover(CROSSOVER_PROBABILITY);
// Mutation operador
var mutate = new SwapMutate(MUTATION_PROBABILITY);
// GA
var ga = new GeneticAlgorithm(population, CalculateFitness);
// Add operators
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutate);
// Handlers
ga.OnGenerationComplete += ga_OnGenerationComplete;
ga.OnRunComplete += ga_OnRunComplete;
ga.Run(Terminate);
Console.ReadLine();
}
/// <summary>
/// Calculate a WoC solution from a population
/// Each gene value is the most popular value for a gene in that position across the population
/// </summary>
/// <param name="pop">The input population. Must contain individuals</param>
/// <returns>A chromosome representing the WoC solution</returns>
private Chromosome CalculateWoC(Population pop)
{
// Create a hash table. Columns are gene positions and rows are possible gene values
// The values are the number of times that gene value occured in that position in the genome
byte[,] hash = new byte[numIntsInSolution, solutionValueMax + 1];
int i = 0, j = 0;
// Fill the hash table
foreach (Chromosome chrom in pop.Solutions)
{ // Iterate through all individuals
i = 0; // i tracks the current gene position in this individual
foreach (Gene gene in chrom)
{ // Iterate through this individuals genes
hash[i, (int)gene.ObjectValue]++; // Increment the correct location in the hash table
i++; // Increment our iterator
}
}
// Create an empty Chromosome to fill and return
Chromosome retChrom = new Chromosome();
// Iterate over hash table again calculating values for return chromosome
for (i = 0; i < numIntsInSolution; i++)
{ // Iterate through columns, which are gene positions that will be colapsed into single gene values based on which value is most populat in that position
int mostPop = -1;
int timesRepeated = -1;
for (j = 0; j < solutionValueMax; j++)
{ // Iterate through the rows in this column, finding the most popular value
// Make current value the most popular value if it's been here more times. If they're tied choose randomly
if (hash[i, j] > timesRepeated || (hash[i, j] == timesRepeated && rand.Next(0, 1) == 1))
{
timesRepeated = hash[i, j];
mostPop = j;
}
}
// If we don't have any most popular value, the column was empty, something went very wrong, throw an exception.
// (This shouldn't be able to happen, but it's still good to double check, as it might take some time to find this otherwise)
if (mostPop == -1)
{
throw new ApplicationException("Something went very wrong in WoC");
}
// Add this most popular gene value to the return chromosome
var newGene = new Gene(mostPop);
retChrom.Add(newGene);
}
// Let the WoC chromosome we built calculate it's fitness
retChrom.Evaluate(CalculateFitness);
// Return the WoC chromosome we built
return(retChrom);
}
private Chromosome CopyChromosome(Chromosome toCopy)
{
var result = new Chromosome();
for (int i = 0; i < toCopy.Genes.Count; i++)
{
result.Add(toCopy.Genes[i]);
}
return(result);
}
protected Chromosome GenerateRandomChromosome(int size)
{
var chromosome = new Chromosome();
for (var i = 0; i < size; i++)
{
var gene = RandomGenerator.Next(NumberOfColors);
chromosome.Add(gene);
}
return(chromosome);
}
private Population GenerateInitialPopulation(int popSize)
{
var population = new Population();
for (int i = 0; i < popSize; i++)
{
var chromosome = new Chromosome();
for (int j = 0; j < numIntsInSolution; j++)
{
chromosome.Add(new Gene(rand.Next(solutionValueMin, solutionValueMax)));
}
population.Solutions.Add(chromosome);
}
return(population);
}
/// <summary>
/// Mutate the genome by adding a neuron.
/// </summary>
///
/// <param name="mutationRate">The mutation rate.</param>
/// <param name="numTrysToFindOldLink">The number of tries to find a link to split.</param>
internal void AddNeuron(double mutationRate, int numTrysToFindOldLink)
{
// should we add a neuron?
if (ThreadSafeRandom.NextDouble() > mutationRate)
{
return;
}
int countTrysToFindOldLink = numTrysToFindOldLink;
// the link to split
NEATLinkGene splitLink = null;
int sizeBias = inputCount + outputCount + 10;
// if there are not at least
int upperLimit;
if (linksChromosome.Size() < sizeBias)
{
upperLimit = NumGenes - 1 - (int)Math.Sqrt(NumGenes);
}
else
{
upperLimit = NumGenes - 1;
}
while ((countTrysToFindOldLink--) > 0)
{
// choose a link, use the square root to prefer the older links
int i = RangeRandomizer.RandomInt(0, upperLimit);
var link = (NEATLinkGene)linksChromosome
.Get(i);
// get the from neuron
long fromNeuron = link.FromNeuronID;
if ((link.Enabled) &&
(!link.Recurrent) &&
(((NEATNeuronGene)Neurons.Get(
GetElementPos(fromNeuron))).NeuronType != NEATNeuronType.Bias))
{
splitLink = link;
break;
}
}
if (splitLink == null)
{
return;
}
splitLink.Enabled = false;
double originalWeight = splitLink.Weight;
long from = splitLink.FromNeuronID;
long to = splitLink.ToNeuronID;
var fromGene = (NEATNeuronGene)Neurons.Get(
GetElementPos(from));
var toGene = (NEATNeuronGene)Neurons.Get(
GetElementPos(to));
double newDepth = (fromGene.SplitY + toGene.SplitY) / 2;
double newWidth = (fromGene.SplitX + toGene.SplitX) / 2;
// has this innovation already been tried?
NEATInnovation innovation = ((NEATTraining)GA).Innovations.CheckInnovation(from, to,
NEATInnovationType
.NewNeuron);
// prevent chaining
if (innovation != null)
{
long neuronID = innovation.NeuronID;
if (AlreadyHaveThisNeuronID(neuronID))
{
innovation = null;
}
}
if (innovation == null)
{
// this innovation has not been tried, create it
long newNeuronID = ((NEATTraining)GA).Innovations.CreateNewInnovation(from, to,
NEATInnovationType.
NewNeuron,
NEATNeuronType.
Hidden,
newWidth, newDepth);
neuronsChromosome.Add(new NEATNeuronGene(
NEATNeuronType.Hidden, newNeuronID, newDepth, newWidth));
// add the first link
long link1ID = (GA).Population.AssignInnovationID();
((NEATTraining)GA).Innovations
.CreateNewInnovation(from, newNeuronID,
NEATInnovationType.NewLink);
var link1 = new NEATLinkGene(from, newNeuronID,
true, link1ID, 1.0d, false);
linksChromosome.Add(link1);
// add the second link
long link2ID = (GA).Population.AssignInnovationID();
((NEATTraining)GA).Innovations
.CreateNewInnovation(newNeuronID, to,
NEATInnovationType.NewLink);
var link2 = new NEATLinkGene(newNeuronID, to, true,
link2ID, originalWeight, false);
linksChromosome.Add(link2);
}
else
{
// existing innovation
long newNeuronID_0 = innovation.NeuronID;
NEATInnovation innovationLink1 = ((NEATTraining)GA).Innovations.CheckInnovation(from,
newNeuronID_0,
NEATInnovationType
.
NewLink);
NEATInnovation innovationLink2 =
((NEATTraining)GA).Innovations.CheckInnovation(newNeuronID_0, to,
NEATInnovationType.NewLink);
if ((innovationLink1 == null) || (innovationLink2 == null))
{
throw new NeuralNetworkError("NEAT Error");
}
var link1_1 = new NEATLinkGene(from, newNeuronID_0,
true, innovationLink1.InnovationID, 1.0d, false);
var link2_2 = new NEATLinkGene(newNeuronID_0, to, true,
innovationLink2.InnovationID, originalWeight, false);
linksChromosome.Add(link1_1);
linksChromosome.Add(link2_2);
var newNeuron = new NEATNeuronGene(
NEATNeuronType.Hidden, newNeuronID_0, newDepth, newWidth);
neuronsChromosome.Add(newNeuron);
}
return;
}
/// <summary>
/// Mutate the genome by adding a link to this genome.
/// </summary>
///
/// <param name="mutationRate">The mutation rate.</param>
/// <param name="chanceOfLooped">The chance of a self-connected neuron.</param>
/// <param name="numTrysToFindLoop">The number of tries to find a loop.</param>
/// <param name="numTrysToAddLink">The number of tries to add a link.</param>
internal void AddLink(double mutationRate, double chanceOfLooped,
int numTrysToFindLoop, int numTrysToAddLink)
{
// should we even add the link
if (ThreadSafeRandom.NextDouble() > mutationRate)
{
return;
}
int countTrysToFindLoop = numTrysToFindLoop;
int countTrysToAddLink = numTrysToFindLoop;
// the link will be between these two neurons
long neuron1ID = -1;
long neuron2ID = -1;
bool recurrent = false;
// a self-connected loop?
if (ThreadSafeRandom.NextDouble() < chanceOfLooped)
{
// try to find(randomly) a neuron to add a self-connected link to
while ((countTrysToFindLoop--) > 0)
{
NEATNeuronGene neuronGene = ChooseRandomNeuron(false);
// no self-links on input or bias neurons
if (!neuronGene.Recurrent &&
(neuronGene.NeuronType != NEATNeuronType.Bias) &&
(neuronGene.NeuronType != NEATNeuronType.Input))
{
neuron1ID = neuronGene.Id;
neuron2ID = neuronGene.Id;
neuronGene.Recurrent = true;
recurrent = true;
countTrysToFindLoop = 0;
}
}
}
else
{
// try to add a regular link
while ((countTrysToAddLink--) > 0)
{
NEATNeuronGene neuron1 = ChooseRandomNeuron(true);
NEATNeuronGene neuron2 = ChooseRandomNeuron(false);
if (!IsDuplicateLink(neuron1ID, neuron2ID) &&
(neuron1.Id != neuron2.Id) &&
(neuron2.NeuronType != NEATNeuronType.Bias))
{
neuron1ID = neuron1.Id;
neuron2ID = neuron2.Id;
break;
}
}
}
// did we fail to find a link
if ((neuron1ID < 0) || (neuron2ID < 0))
{
return;
}
// check to see if this innovation has already been tried
NEATInnovation innovation = ((NEATTraining)GA).Innovations.CheckInnovation(neuron1ID,
neuron1ID,
NEATInnovationType
.NewLink);
// see if this is a recurrent(backwards) link
var neuronGene_0 = (NEATNeuronGene)neuronsChromosome
.Get(GetElementPos(neuron1ID));
if (neuronGene_0.SplitY > neuronGene_0.SplitY)
{
recurrent = true;
}
// is this a new innovation?
if (innovation == null)
{
// new innovation
((NEATTraining)GA).Innovations
.CreateNewInnovation(neuron1ID, neuron2ID,
NEATInnovationType.NewLink);
long id2 = GA.Population.AssignInnovationID();
var linkGene = new NEATLinkGene(neuron1ID,
neuron2ID, true, id2, RangeRandomizer.Randomize(-1, 1),
recurrent);
linksChromosome.Add(linkGene);
}
else
{
// existing innovation
var linkGene_1 = new NEATLinkGene(neuron1ID,
neuron2ID, true, innovation.InnovationID,
RangeRandomizer.Randomize(-1, 1), recurrent);
linksChromosome.Add(linkGene_1);
}
}
/// <summary>
/// Perform a cross over.
/// </summary>
/// <param name="mom">The mother genome.</param>
/// <param name="dad">The father genome.</param>
/// <returns></returns>
public new NEATGenome Crossover(NEATGenome mom, NEATGenome dad)
{
NEATParent best;
// first determine who is more fit, the mother or the father?
if (mom.Score == dad.Score)
{
if (mom.NumGenes == dad.NumGenes)
{
if (ThreadSafeRandom.NextDouble() > 0)
{
best = NEATParent.Mom;
}
else
{
best = NEATParent.Dad;
}
}
else
{
if (mom.NumGenes < dad.NumGenes)
{
best = NEATParent.Mom;
}
else
{
best = NEATParent.Dad;
}
}
}
else
{
if (Comparator.IsBetterThan(mom.Score, dad.Score))
{
best = NEATParent.Mom;
}
else
{
best = NEATParent.Dad;
}
}
var babyNeurons = new Chromosome();
var babyGenes = new Chromosome();
var vecNeurons = new List <long>();
int curMom = 0;
int curDad = 0;
NEATLinkGene momGene;
NEATLinkGene dadGene;
NEATLinkGene selectedGene = null;
while ((curMom < mom.NumGenes) || (curDad < dad.NumGenes))
{
if (curMom < mom.NumGenes)
{
momGene = (NEATLinkGene)mom.Links.Get(curMom);
}
else
{
momGene = null;
}
if (curDad < dad.NumGenes)
{
dadGene = (NEATLinkGene)dad.Links.Get(curDad);
}
else
{
dadGene = null;
}
if ((momGene == null) && (dadGene != null))
{
if (best == NEATParent.Dad)
{
selectedGene = dadGene;
}
curDad++;
}
else if ((dadGene == null) && (momGene != null))
{
if (best == NEATParent.Mom)
{
selectedGene = momGene;
}
curMom++;
}
else if (momGene.InnovationId < dadGene.InnovationId)
{
if (best == NEATParent.Mom)
{
selectedGene = momGene;
}
curMom++;
}
else if (dadGene.InnovationId < momGene.InnovationId)
{
if (best == NEATParent.Dad)
{
selectedGene = dadGene;
}
curDad++;
}
else if (dadGene.InnovationId == momGene.InnovationId)
{
if (ThreadSafeRandom.NextDouble() < 0.5f)
{
selectedGene = momGene;
}
else
{
selectedGene = dadGene;
}
curMom++;
curDad++;
}
if (babyGenes.Size() == 0)
{
babyGenes.Add(selectedGene);
}
else
{
if (((NEATLinkGene)babyGenes.Get(babyGenes.Size() - 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.FromNeuronID, vecNeurons);
AddNeuronID(selectedGene.ToNeuronID, vecNeurons);
} // end while
// now create the required nodes. First sort them into order
vecNeurons.Sort();
for (int i = 0; i < vecNeurons.Count; i++)
{
babyNeurons.Add(Innovations.CreateNeuronFromID(
vecNeurons[i]));
}
// finally, create the genome
var babyGenome = new NEATGenome(Population
.AssignGenomeID(), babyNeurons, babyGenes, mom.InputCount,
mom.OutputCount);
babyGenome.GA = this;
babyGenome.Population = Population;
return(babyGenome);
}
