/// <summary>
/// Initializes a new instance of the <see cref="AutoConfigFitness"/> class.
/// </summary>
/// <param name="targetFitness">The target fitness.</param>
/// <param name="targetChromosome">The target chromosome.</param>
public AutoConfigFitness(IFitness targetFitness, IChromosome targetChromosome)
{
m_targetFitness = targetFitness;
m_targetChromosome = targetChromosome;
PopulationMinSize = 100;
PopulationMaxSize = 100;
Termination = new TimeEvolvingTermination(TimeSpan.FromSeconds(30));
TaskExecutor = new LinearTaskExecutor();
}
public void Start_Task_TaskRan()
{
var pipeline = "";
var target = new LinearTaskExecutor();
target.Add(() => pipeline += "1");
target.Add(() => pipeline += "2");
target.Add(() => pipeline += "3");
Assert.IsTrue(target.Start());
Assert.AreEqual("123", pipeline);
}
public void Start_TakeMoreThanTimeout_False()
{
var pipeline = "";
var target = new LinearTaskExecutor ();
target.Add (() => pipeline += "1");
target.Add (() => {
pipeline += "2";
Thread.Sleep(100);
});
target.Add (() => pipeline += "3");
target.Timeout = TimeSpan.FromMilliseconds(50);
Assert.IsFalse(target.Start ());
Assert.AreEqual ("12", pipeline);
}
public void Stop_ManyTasks_True()
{
var pipeline = "";
var target = new LinearTaskExecutor();
target.Add(() => pipeline += "1");
target.Add(() =>
{
pipeline += "2";
Thread.Sleep(1000);
});
target.Add(() => pipeline += "3");
Parallel.Invoke(
() => Assert.IsTrue(target.Start()),
() =>
{
Thread.Sleep(5);
target.Stop();
});
}
/// <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();
}