/// <summary>
/// Linear xover executor.
/// </summary>
/// <param name="population">Input population.</param>
/// <param name="xover">Xover operator.</param>
/// <param name="xoverProbability">Xover probability</param>
/// <param name="parents">Parents list</param>
/// <returns>Childten(individuals)</returns>
public virtual IList <IIndividual> Cross(IPopulation population, IXover xover, float xoverProbability, IList <IIndividual> parents)
{
var size = population.Size;
var offspring = new List <IIndividual>(size);
for (int i = 0; i < size; i += xover.ChildrenNumber)
{
// selected parents from population
var selectedParents = parents.Skip(i).Take(xover.ParentsNumber).ToList();
// If match the probability cross is made, otherwise the offspring is an exact copy of the parents.
// Checks if the number of selected parents is equal which the crossover expect, because the in the end of the list we can
// have some rest individual
if (selectedParents.Count == xover.ParentsNumber && FastRandom.GetDouble() <= xoverProbability)
{
offspring.AddRange(xover.Cross(selectedParents));
}
else
{
offspring.AddRange(selectedParents);
}
}
return(offspring);
}
/// <summary>
/// Constructor for genetic algorithm.
/// Genetic algorithms imitate natural biological processes,
/// </summary>
/// <param name="population">Init population. </param>
/// <param name="fitness">Fitness.</param>
/// <param name="selection">Selection operator.</param>
/// <param name="xover">Xover operator.</param>
/// <param name="mutation">Mutation operator.</param>
/// <param name="elitizmus">Elitizmus.</param>
/// <param name="termination">Termination GA.</param>
/// <param name="executor">Executor.</param>
/// <param name="mutationProbability">Mutation probability.</param>
/// <param name="xoverProbability">Xover probability.</param>
public GeneticAlgorithm(IPopulation population,
IFitness fitness,
ISelection selection,
IXover xover,
IMutation mutation,
IElite elitizmus,
ITermination termination,
IExecutor executor,
float mutationProbability,
float xoverProbability)
{
Population = population;
this.fitness = fitness;
this.selection = selection;
this.xover = xover;
this.mutation = mutation;
this.elitizmus = elitizmus;
this.executor = executor;
this.termination = termination;
// base probability
this.xoverProbability = xoverProbability;
this.mutationProbability = mutationProbability;
TimeEvolving = TimeSpan.Zero;
CurrentGenerationsNumber = 1;
}
/// <summary>
/// Evolutionary strategy
/// </summary>
/// <param name="fitness">fit. func.</param>
/// <param name="population">init pop</param>
public ES_μ_λ(IFitness fitness, IPopulation population) : base(fitness, population)
{
xover = new XoverUniform();
elite = new EliteByFitness(0.1);
selection = new SelectionTournament();
termination = new TerminationMaxNumberGeneration();
termination.InitializeTerminationCondition(15_000);
mutationProbability = 0.01f;
xoverProbability = 0.5f;
}
