public void EndCurrentGeneration_BestChromosomeChanged_ChangeEventRaise()
{
var target = new TplPopulation(2, 2, new ChromosomeStub());
var eventRaise = false;
target.BestChromosomeChanged += (e, a) =>
{
eventRaise = true;
};
target.CreateInitialGeneration();
target.CurrentGeneration.Chromosomes.Each(c => c.Fitness = 1);
target.EndCurrentGeneration();
Assert.IsTrue(eventRaise);
}
public void CreateInitialGeneration_AdamChromosomeCreateNewNull_Exception()
{
var c = Substitute.For <ChromosomeBase>(4);
c.CreateNew().Returns((IChromosome)null);
var population = new TplPopulation(2, 2, c);
Assert.Catch <InvalidOperationException>(() =>
{
try
{
population.CreateInitialGeneration();
}
catch (AggregateException e)
{
throw e.InnerException;
}
}, "The Adam chromosome's 'CreateNew' method generated a null chromosome. This is a invalid behavior, please, check your chromosome code.");
}
public static FloatingPointChromosome EvolveGeneticAlgorithm(FloatingPointChromosome chromosome, IOthelloAgent agent, string chromosomeLabel = "")
{
IPopulation population = new TplPopulation(30, 60, chromosome);
IFitness fitness = new EvaluationFitness(agent);
ISelection selection = new RouletteWheelSelection(); //Guess
ICrossover crossover = new UniformCrossover(); //Guess
IMutation mutation = new UniformMutation(); //Guess
ITermination stagnation = new FitnessStagnationTermination(500);
ITermination threshold = new FitnessThresholdTermination(.9);
ITaskExecutor taskExecutor = new ParallelTaskExecutor()
{
MaxThreads = Environment.ProcessorCount,
MinThreads = Environment.ProcessorCount / 2
};
GeneticAlgorithm algorithm = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
TaskExecutor = new TplTaskExecutor(),
MutationProbability = .2f
};
algorithm.TaskExecutor = taskExecutor;
algorithm.Termination = stagnation;
algorithm.Start();
SaveChromosome((FloatingPointChromosome)algorithm.BestChromosome, chromosomeLabel);
Debug.WriteLine("finished Training, with {0} time spent on evolving", algorithm.TimeEvolving);
Debug.WriteLine("fitness of this generation vs the last : {0}", algorithm.Fitness);
return((FloatingPointChromosome)algorithm.BestChromosome);
}
