public void HasReached_GenerationNumberLowerThanCondition_False()
{
var repository = new MockRepository();
var ga = repository.StrictMock<IGeneticAlgorithm>();
using (repository.Ordered())
{
ga.Expect(g => g.GenerationsNumber).Return(1);
ga.Expect(g => g.GenerationsNumber).Return(2);
ga.Expect(g => g.GenerationsNumber).Return(3);
ga.Expect(g => g.GenerationsNumber).Return(4);
ga.Expect(g => g.GenerationsNumber).Return(5);
ga.Expect(g => g.GenerationsNumber).Return(6);
ga.Expect(g => g.GenerationsNumber).Return(7);
ga.Expect(g => g.GenerationsNumber).Return(8);
ga.Expect(g => g.GenerationsNumber).Return(0);
}
repository.ReplayAll();
var target = new GenerationNumberTermination(10);
Assert.IsFalse(target.HasReached(ga));
for (int i = 0; i < 8; i++)
{
Assert.IsFalse(target.HasReached(ga));
}
}
public void HasReached_GenerationNumberGreaterOrEqualThanCondition_True()
{
var repository = new MockRepository ();
var ga = repository.StrictMock<IGeneticAlgorithm> ();
using (repository.Ordered()) {
ga.Expect (g => g.GenerationsNumber).Return (10);
ga.Expect (g => g.GenerationsNumber).Return (11);
}
repository.ReplayAll ();
var target = new GenerationNumberTermination (10);
Assert.IsTrue (target.HasReached (ga));
Assert.IsTrue (target.HasReached (ga));
}
/// <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();
}
