public Population(int count, IPopulationGenerator <T> generator, IFitnessEvaluator <T> evaluator)
{
if (count <= 0)
{
throw new ArgumentException($"{nameof(count)} must be greater than 0. Actual: {count}.");
}
if (generator == null)
{
throw new System.ArgumentNullException(nameof(generator));
}
if (evaluator == null)
{
throw new System.ArgumentNullException(nameof(evaluator));
}
TargetSize = count;
// TODO: This really shouldn't be here. We should instead pass the
// population
for (var i = 0; i < count; i++)
{
var value = generator.Generate();
var fitness = evaluator.ComputeFitness(value);
var chromosome = new Chromosome <T>(value, fitness);
Chromosomes.Add(chromosome);
}
}
public void Add(I interval, string chr, char strand)
{
if (!Chromosomes.ContainsKey(chr))
{
Chromosomes.Add(chr, new Chromosome <I, S>());
}
Chromosomes[chr].Add(interval, strand);
Statistics.Update(interval);
}
/// <summary>
/// Create a NEAT gnome.
/// </summary>
///
/// <param name="genomeID">The genome id.</param>
/// <param name="neurons">The neurons.</param>
/// <param name="links">The links.</param>
/// <param name="inputCount_0">The input count.</param>
/// <param name="outputCount_1">The output count.</param>
public NEATGenome(long genomeID, Chromosome neurons,
Chromosome links, int inputCount_0, int outputCount_1)
{
GenomeID = genomeID;
linksChromosome = links;
neuronsChromosome = neurons;
AmountToSpawn = 0;
AdjustedScore = 0;
inputCount = inputCount_0;
outputCount = outputCount_1;
Chromosomes.Add(neuronsChromosome);
Chromosomes.Add(linksChromosome);
}
/// <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));
}
}
}
/// <summary>
/// 初始化，设置染色体
/// </summary>
private static void Init()
{
Chromosomes.Clear();
for (int i = 0; i < ChromosomeLength; i++)
{
int[,] valueLimit = new int[GenesLength, 2];
for (int ii = 0; ii < GenesLength; ii++)
{
valueLimit[ii, 0] = variateInfo[ii].MinValue;
valueLimit[ii, 1] = variateInfo[ii].MaxValue;
}
Chromosome chromosome = new Chromosome {
ValueLimit = valueLimit,
};
chromosome.Genes = IniVariate(chromosome);
Chromosomes.Add(chromosome);
}
}
/// <summary>
/// Construct a genome by copying another.
/// </summary>
///
/// <param name="other">The other genome.</param>
public NEATGenome(NEATGenome other)
{
neuronsChromosome = new Chromosome();
linksChromosome = new Chromosome();
GA = other.GA;
Chromosomes.Add(neuronsChromosome);
Chromosomes.Add(linksChromosome);
GenomeID = other.GenomeID;
networkDepth = other.networkDepth;
Population = other.Population;
Score = other.Score;
AdjustedScore = other.AdjustedScore;
AmountToSpawn = other.AmountToSpawn;
inputCount = other.inputCount;
outputCount = other.outputCount;
speciesID = other.speciesID;
// copy neurons
foreach (IGene gene in other.Neurons.Genes)
{
var oldGene = (NEATNeuronGene)gene;
var newGene = new NEATNeuronGene(
oldGene.NeuronType, oldGene.Id,
oldGene.SplitY, oldGene.SplitX,
oldGene.Recurrent, oldGene.ActivationResponse);
Neurons.Add(newGene);
}
// copy links
foreach (IGene gene_0 in other.Links.Genes)
{
var oldGene_1 = (NEATLinkGene)gene_0;
var newGene_2 = new NEATLinkGene(
oldGene_1.FromNeuronID, oldGene_1.ToNeuronID,
oldGene_1.Enabled, oldGene_1.InnovationId,
oldGene_1.Weight, oldGene_1.Recurrent);
Links.Add(newGene_2);
}
}
/// <summary>
/// Construct a neural genome.
/// </summary>
///
/// <param name="network">The network to use.</param>
public NeuralGenome(BasicNetwork network)
{
Organism = network;
_networkChromosome = new Chromosome();
// create an array of "double genes"
int size = network.Structure.CalculateSize();
for (int i = 0; i < size; i++)
{
IGene gene = new DoubleGene();
_networkChromosome.Genes.Add(gene);
}
Chromosomes.Add(_networkChromosome);
Encode();
}
public TSPGenome(GeneticAlgorithm owner, City[] cities)
{
var organism = new int[cities.Length];
var taken = new bool[cities.Length];
for (int i = 0; i < organism.Length; i++)
{
taken[i] = false;
}
for (int i = 0; i < organism.Length - 1; i++)
{
int icandidate;
do
{
icandidate = (int)(ThreadSafeRandom.NextDouble() * organism.Length);
} while (taken[icandidate]);
organism[i] = icandidate;
taken[icandidate] = true;
if (i == organism.Length - 2)
{
icandidate = 0;
while (taken[icandidate])
{
icandidate++;
}
organism[i + 1] = icandidate;
}
}
pathChromosome = new Chromosome();
Chromosomes.Add(pathChromosome);
for (int i = 0; i < organism.Length; i++)
{
var gene = new IntegerGene();
gene.Value = organism[i];
pathChromosome.Genes.Add(gene);
}
Organism = organism;
Encode();
}
//随机生成若干染色体
public void RandomGenerateChromosome()
{
var rnd = new Random();
for (var i = 0; i < ChromosomeQuantity; i++)
{
var chromosome = new Chromosome {
Population = this
};
var valueString = "";
for (var k = 0; k < ChromosomeLength; k++)
{
valueString += Convert.ToInt32(rnd.Next(0, 2)).ToString();
}
chromosome.Value = Convert.ToInt64(valueString, 2);
Chromosomes.Add(chromosome);
}
}
public Population(int count, IPopulationGenerator <T> generator, IFitnessEvaluator <T> evaluator)
{
Contract.Requires <ArgumentOutOfRangeException>(count > 0);
Contract.Requires <ArgumentNullException>(generator != null);
Contract.Requires <ArgumentNullException>(evaluator != null);
TargetSize = count;
Evaluator = evaluator;
// TODO: This really shouldn't be here. We should instead pass the
// population
for (var i = 0; i < count; i++)
{
var value = generator.Generate();
var chromosome = new Chromosome <T>(value);
chromosome.Fitness = evaluator.ComputeFitness(chromosome.Genes);
Chromosomes.Add(chromosome);
}
}
public void NextGeneration(ISelection selectionScheme)
{
var selected = selectionScheme.Select(Chromosomes, 5).OrderByDescending(s => s.Cost).ToList();
Chromosomes.Remove(selected[0]);
Chromosomes.Remove(selected[1]);
// fill the remaining empty space with children generated from selected chromosomes
Chromosome parent1 = selected[3];
Chromosome parent2 = selected[4];
Chromosome child1 = OrderedCrossover.MakeChild(parent1, parent2);
Chromosome child2 = OrderedCrossover.MakeChild(parent2, parent1);
if (ThreadSafeRandom.CurrentThreadRandom.Next(100) < 3)
{
child1.Mutate();
}
if (ThreadSafeRandom.CurrentThreadRandom.Next(100) < 3)
{
child2.Mutate();
}
Chromosomes.Add(child1);
Chromosomes.Add(child2);
double currentTour = Chromosomes.Where(c => c.Cost > 0).Min(c => c.Cost);
if (currentTour < BestTour)
{
BestTour = currentTour;
Console.WriteLine("Generation " + currentGeneration + " Best: " + BestTour);
}
currentGeneration++;
}
public void AddChromosome(T chromosome)
{
Chromosomes.Add(chromosome);
}
public State <T> AddChromosome(Chromosome <T> chromosome)
{
var newChromosomes = Chromosomes.Add(chromosome).OrderByDescending(c => c.Fitness);
return(new State <T>(newChromosomes));
}
public void AddChromosome(Chromosome <T> chromosome)
{
Contract.Requires <ArgumentNullException>(chromosome != null, "Chromosome cannot be null.");
Chromosomes.Add(chromosome);
}
