private Rabbit()
{
_fitness = -1;
_dna = new Dna {
Gene = (List <int>)Enumerable.Empty <int>()
};
}
/// <summary>
///
/// </summary>
/// <param name="dna"></param>
/// <returns></returns>
public override double GetFitness(IDna dna)
{
var fitness = dna.Gene.Sum() / (double)dna.Gene.Count;
if (Math.Abs(fitness - SolutionValue) > 0.0)
{
return(dna.Gene.Sum() / (double)dna.Gene.Count);
}
Stop?.Invoke(dna);
return(1);
}
/// <summary>
/// Tworzy nową generację z określoną ilością obiektów oraz kontrolerem ewolucyjnym.
/// </summary>
/// <param name="evolutionController">Kontroler ewolucyjny.</param>
/// <param name="size">Ilość obiektów.</param>
public Generation(IEvolutionController evolutionController, uint size)
{
Objects = new IDna[size];
this.evolutionController = evolutionController ?? throw new ArgumentNullException("evolutionController nie może być null.");
for (int i = 0; i < size; i++)
{
Objects[i] = evolutionController.GetRandomDna();
Objects[i].Generation = this;
Objects[i].Id = i;
}
}
public override double GetFitness(IDna dna)
{
var s = dna.Gene.Select((gen, index) => gen * index).Sum();
var result = (int)(s - SolutionValue);
if (result > 0.0)
{
return(Math.Abs(1 / (s - SolutionValue)));
}
Stop?.Invoke(dna);
return(1);
}
public IDna <Vector2> Crossover(IDna <Vector2> partner)
{
var child = new VectorDna(Genes.Length);
var midpoint = Convert.ToInt32(Random.value * Genes.Length);
for (var i = 0; i < Genes.Length; i++)
{
if (i < midpoint)
{
child.Genes[i] = Genes[i];
}
else
{
child.Genes[i] = partner.Genes[i];
}
}
return(child);
}
public IDna <char> Crossover(IDna <char> partner)
{
var midpoint = Random.Next(Genes.Length);
var child = new PhraseDna(Genes.Length);
for (var i = 0; i < Genes.Length; i++)
{
if (i < midpoint)
{
child.Genes[i] = partner.Genes[i];
}
else
{
child.Genes[i] = Genes[i];
}
}
return(child);
}
/// <summary>
/// Tworzy nową generację krzyżując obiekty z obecnej generacji.
/// </summary>
/// <returns>Zwraca nową generację.</returns>
public Generation CreateNewGeneration()
{
if (!CurrentGeneration.WasEvaluated)
{
CurrentGeneration.Evaluate();
}
IDna[] objects = new IDna[GenerationsSize];
for (uint i = 0; i < GenerationsSize; i++)
{
IDna parentA = CurrentGeneration.GetDnaBasedOnFitness();
IDna parentB = CurrentGeneration.GetDnaBasedOnFitness();
objects[i] = evolutionController.Crossover(parentA, parentB);
IDna db = objects[i];
Console.WriteLine(new string(db.GetGenes() as char[]) + " ||| " + db.Fitness);
}
CurrentGeneration = new Generation(evolutionController, objects);
CurrentGeneration.Mutate(MutationChance > 100 ? (byte)100 : MutationChance);
return(CurrentGeneration);
}
static void TestGeneticAlgorithm()
{
string target = "Be or not to be?";
IEvolutionController evolutionController = new EvolutionController(target);
Population population = new Population(evolutionController, 300);
population.MutationChance = 10;
population.CurrentGeneration.Evaluate();
while (true)
{
ReadKey();
population.CurrentGeneration.Evaluate();
IDna db = population.CurrentGeneration.Best;
WriteLine(new string(db.GetGenes() as char[]) + " ||| " + db.Fitness);
population.CreateNewGeneration();
}
}
// Zaimplementowane z IEvolutionController
public IDna Crossover(IDna parentA, IDna parentB)
{
IDna child = new Dna(target, target.Length);
char[] childGenes = child.GetGenes() as char[];
char[] aGenes = parentA.GetGenes() as char[];
char[] bGenes = parentB.GetGenes() as char[];
int midpoint = childGenes.Length / 2;
for (int i = 0; i < childGenes.Length; i++)
{
if (i < midpoint)
{
childGenes[i] = aGenes[i];
}
else
{
childGenes[i] = bGenes[i];
}
}
return(child);
}
public abstract double GetFitness(IDna dna);
private static void StopAlgorithm(IDna dna)
{
Console.WriteLine("The genome satisfy fitness criteria: ({0}), dna: {1}", FitnessSolution, string.Join(" ", dna.Gene));
}
public Rabbit(IDna dna, double fitness)
{
_dna = dna;
_fitness = fitness;
}
private static void StopAlgorithm(IDna dna)
{
var gene = dna.Gene;
Console.WriteLine("FIND SOLUTION:: {0} + 2*{1} + 3*{2} + 4*{3}", gene[0], gene[1], gene[2], gene[3]);
}
