public override ITermination CreateTermination()
{
var terminations = new TimeEvolvingTermination(TimeSpan.FromSeconds(this.settings.MaxOptimizationTimeSeconds));
var fitnessStagnationTermination = new FitnessStagnationTermination(this.settings.MaxOptimizationStagnatingGenerations);
return(new OrTermination(terminations, fitnessStagnationTermination));
}
/// <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 ITermination TimeEvolving()
{
var target = new TimeEvolvingTermination(TimeSpan.FromMilliseconds(1));
var ga = Substitute.For <IGeneticAlgorithm>();
target.HasReached(ga);
return(target);
}
public void HasReached_TimeLowerThanMaxTime_False()
{
var ga = Substitute.For <IGeneticAlgorithm>();
ga.TimeEvolving.Returns(TimeSpan.FromSeconds(0.1),
TimeSpan.FromSeconds(00.9));
var target = new TimeEvolvingTermination(TimeSpan.FromSeconds(1));
Assert.IsFalse(target.HasReached(ga));
Assert.IsFalse(target.HasReached(ga));
}
public void HasReached_TimeLowerThanMaxTime_False()
{
var repository = new MockRepository();
var ga = repository.StrictMock <IGeneticAlgorithm> ();
using (repository.Ordered()) {
ga.Expect(g => g.TimeEvolving).Return(TimeSpan.FromSeconds(0.1));
ga.Expect(g => g.TimeEvolving).Return(TimeSpan.FromSeconds(0.9));
}
repository.ReplayAll();
var target = new TimeEvolvingTermination(TimeSpan.FromSeconds(1));
Assert.IsFalse(target.HasReached(ga));
Assert.IsFalse(target.HasReached(ga));
}
public Task <Timetable> RunAsync()
{
var adamChromosome = CreateAdamChromosome();
var population = new Population(50, 100, adamChromosome);
var fitness = new FuncFitness(TimetablerFitnessFunction);
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new FlipBitMutation();
var termination = new TimeEvolvingTermination(TimeSpan.FromSeconds(20));
var geneticAlgorithm = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation)
{
Termination = termination
};
geneticAlgorithm.GenerationRan += (sender, e) =>
{
var bestChromosome = geneticAlgorithm.BestChromosome as FloatingPointChromosome;
GeneticSolution = ChromosomeToTimetable(bestChromosome);
};
GeneticAlgorithm = geneticAlgorithm;
return(Task.Factory.StartNew(() =>
{
geneticAlgorithm.Start();
return GeneticSolution;
}));
}
