/// <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;
}
/// <summary>
/// Creates a new population.
/// </summary>
/// <param name="population">The current population.</param>
/// <param name="elite">The elite operator.</param>
/// <param name="mutate">The mutate operator.</param>
/// <param name="crossover">The crossover operator.</param>
/// <param name="diversify">The diversify operator.</param>
/// <returns></returns>
public Population CreateNewPopulation(Population population, IElite elite, IMutate mutate, ICrossOver crossover, IDiversify diversify)
{
IChromosome parent1, parent2;
IChromosome child1 = null, child2 = null;
// create a new population
var nextPopulation = new Population();
// copy all elites to the new population
elite?.Process(population, nextPopulation);
// make sure the population is diversified enough
diversify?.Process(nextPopulation, population.Count);
// while next population is not filled completely
while (nextPopulation.Count < population.Count)
{
// select 2 parents
DoTournament(population, out parent1, out parent2);
// perform crossover so we get 2 children
crossover?.Process(parent1, parent2, out child1, out child2);
// mutate both children
mutate?.Process(child1);
mutate?.Process(child2);
// and add the children to the next population
nextPopulation.Add(child1);
if (nextPopulation.Count < population.Count)
{
nextPopulation.Add(child2);
}
}
return(nextPopulation);
}
/// <summary>
/// Adds the specified elite operator
/// </summary>
/// <param name="elite">The elite operator</param>
public void Add(IElite elite)
{
_elite = elite;
}
