public void Evolve_ManyGenerations_Fast()
{
int numberOfCities = 40;
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new TworsMutation();
var chromosome = new TspChromosome(numberOfCities);
var fitness = new TspFitness (numberOfCities, 0, 1000, 0, 1000);
var population = new Population (40, 40, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Start();
var firstDistance = ((TspChromosome)ga.Population.BestChromosome).Distance;
ga.Termination = new GenerationNumberTermination (1001);
TimeAssert.LessThan(3000, () =>
{
ga.Start();
});
var lastDistance = ((TspChromosome)ga.Population.BestChromosome).Distance;
Assert.Less(lastDistance, firstDistance);
}
public void Evolve_ManyGenerations_Fast()
{
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation(true);
var chromosome = new AutoConfigChromosome();
var targetChromosome = new TspChromosome(10);
var targetFitness = new TspFitness(10, 0, 100, 0, 100);
var fitness = new AutoConfigFitness(targetFitness, targetChromosome);
fitness.PopulationMinSize = 20;
fitness.PopulationMaxSize = 20;
fitness.Termination = new TimeEvolvingTermination(TimeSpan.FromSeconds(5));
var population = new Population(10, 10, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.TaskExecutor = new SmartThreadPoolTaskExecutor()
{
MinThreads = 10,
MaxThreads = 20
};
ga.Termination = new GenerationNumberTermination(2);
ga.Start();
Assert.NotNull(ga.BestChromosome);
}
/// <summary>
/// Creates the fitness.
/// </summary>
/// <returns>The fitness.</returns>
public override IFitness CreateFitness()
{
var r = Context.DrawingArea;
m_fitness = new TspFitness(m_numberOfCities, r.Left, r.Right, r.Top, r.Bottom);
return m_fitness;
}
public void Evaluate_FitnessLowerThanZero_Zero()
{
var target = new TspFitness(10, 0, 10000000, 0, 10000000);
var chromosome = new TspChromosome(10);
var actual = target.Evaluate(chromosome);
Assert.AreEqual(0, actual);
}
public void Evaluate_ChromosomeWithLowerCities_FitnessDividedByDiff()
{
var target = new TspFitness(10, 0, 10, 0, 10);
var chromosome = new TspChromosome(9);
var actual = target.Evaluate(chromosome);
Assert.AreNotEqual(0, actual);
}
public override IFitness CreateFitness()
{
var targetChromosome = new TspChromosome(10);
var targetFitness = new TspFitness(10, 0, 100, 0, 100);
var fitness = new AutoConfigFitness(targetFitness, targetChromosome);
fitness.Termination = new FitnessStagnationTermination(500);
fitness.PopulationMinSize = 20;
fitness.PopulationMaxSize = 20;
return fitness;
}
public override IFitness CreateFitness()
{
m_fitness = new TspFitness(m_numberOfCities, 0, 1000, 0, 1000);
return m_fitness;
}
