public void SelectChromosomes_offspringSizeLowerThanMinSize_Selectoffspring()
{
var target = new ElitistReinsertion ();
var population = new Population (6, 8, MockRepository.GenerateStub<ChromosomeBase> (2));
var offspring = new List<IChromosome> () {
MockRepository.GenerateStub<ChromosomeBase> (2),
MockRepository.GenerateStub<ChromosomeBase> (2),
MockRepository.GenerateStub<ChromosomeBase> (3),
MockRepository.GenerateStub<ChromosomeBase> (4)
};
var parents = new List<IChromosome> () {
MockRepository.GenerateStub<ChromosomeBase> (5),
MockRepository.GenerateStub<ChromosomeBase> (6),
MockRepository.GenerateStub<ChromosomeBase> (7),
MockRepository.GenerateStub<ChromosomeBase> (8)
};
parents [0].Fitness = 0.2;
parents [1].Fitness = 0.3;
parents [2].Fitness = 0.5;
parents [3].Fitness = 0.7;
var selected = target.SelectChromosomes (population, offspring, parents);
Assert.AreEqual (6, selected.Count);
Assert.AreEqual (2, selected [0].Length);
Assert.AreEqual (2, selected [1].Length);
Assert.AreEqual (3, selected [2].Length);
Assert.AreEqual (4, selected [3].Length);
Assert.AreEqual (8, selected [4].Length);
Assert.AreEqual (7, selected [5].Length);
}
/// <summary>
/// Initializes a new instance of the <see cref="GeneticSharp.Domain.GeneticAlgorithm"/> class.
/// </summary>
/// <param name="population">The chromosomes population.</param>
/// <param name="fitness">The fitness evaluation function.</param>
/// <param name="selection">The selection operator.</param>
/// <param name="crossover">The crossover operator.</param>
/// <param name="mutation">The mutation operator.</param>
public GeneticAlgorithm(
Population population,
IFitness fitness,
ISelection selection,
ICrossover crossover,
IMutation mutation)
{
ExceptionHelper.ThrowIfNull("Population", population);
ExceptionHelper.ThrowIfNull("fitness", fitness);
ExceptionHelper.ThrowIfNull("selection", selection);
ExceptionHelper.ThrowIfNull("crossover", crossover);
ExceptionHelper.ThrowIfNull("mutation", mutation);
Population = population;
Fitness = fitness;
Selection = selection;
Crossover = crossover;
Mutation = mutation;
Reinsertion = new ElitistReinsertion();
Termination = new GenerationNumberTermination(1);
CrossoverProbability = DefaultCrossoverProbability;
MutationProbability = DefaultMutationProbability;
TimeEvolving = TimeSpan.Zero;
State = GeneticAlgorithmState.NotStarted;
TaskExecutor = new LinearTaskExecutor();
}