public void Start_ParallelManySlowFitness_Timeout()
{
var taskExecutor = new SmartThreadPoolTaskExecutor();
taskExecutor.MinThreads = 100;
taskExecutor.MaxThreads = 100;
taskExecutor.Timeout = TimeSpan.FromMilliseconds(1000);
var selection = new RouletteWheelSelection();
var crossover = new OnePointCrossover(1);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 150, chromosome),
new FitnessStub()
{
SupportsParallel = true, ParallelSleep = 1500
}, selection, crossover, mutation);
target.TaskExecutor = taskExecutor;
ExceptionAssert.IsThrowing(new TimeoutException("The fitness evaluation rech the 00:00:01 timeout."), () =>
{
target.Start();
});
Assert.IsFalse(target.IsRunning);
Assert.AreEqual(GeneticAlgorithmState.Stopped, target.State);
}
private void Optimize_Load(object sender, EventArgs e)
{
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new TrajectoryFitness();
var chromosome = new TrajectoryChromosome(TrajectoryChromosome.ChromosomeLength(3), 3);
var population = new Population(35, 40, chromosome);
//72 (6)
//67 (2)
//49 (3)
//102 (1)
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
var executor = new SmartThreadPoolTaskExecutor();
executor.MinThreads = 1;
executor.MaxThreads = 1;
ga.TaskExecutor = executor;
ga.Termination = new GenerationNumberTermination(10);
Console.WriteLine("GA running...");
var t = DateTime.Now;
ga.Start();
Console.WriteLine("Time:" + DateTime.Now.Subtract(t).TotalSeconds);
Console.WriteLine("Best solution found has {0} fitness.", int.MaxValue - ga.BestChromosome.Fitness);
Console.WriteLine(ga.BestChromosome.GetGene(0).Value + " " + ga.BestChromosome.GetGene(1).Value + " " + ga.BestChromosome.GetGene(2).Value);
}
public void Start_AnyTaskWithException_Exception()
{
var pipeline = "";
var target = new SmartThreadPoolTaskExecutor();
target.Add(() =>
{
throw new Exception("1");
});
target.Add(() =>
{
Thread.Sleep(5);
pipeline += "2";
});
target.Add(() =>
{
Thread.Sleep(5);
pipeline += "3";
});
ExceptionAssert.IsThrowing(new Exception("1"), () =>
{
target.Start();
});
}
public void Stop_ManyTasks_StopAll()
{
var pipeline = "";
var target = new SmartThreadPoolTaskExecutor();
target.Timeout = TimeSpan.FromMilliseconds(1000);
target.Add(() =>
{
Thread.Sleep(5);
pipeline += "1";
});
target.Add(() =>
{
Thread.Sleep(5);
pipeline += "2";
});
target.Add(() =>
{
Thread.Sleep(5);
pipeline += "3";
});
Parallel.Invoke(
() => Assert.IsTrue(target.Start()),
() =>
{
Thread.Sleep(100);
target.Stop();
});
}
public void Stop_ManyTasks_StopAll()
{
var pipeline = "";
var target = new SmartThreadPoolTaskExecutor();
target.Timeout = TimeSpan.FromMilliseconds(1000);
target.Add(() =>
{
Thread.Sleep(5);
pipeline += "1";
});
target.Add(() =>
{
Thread.Sleep(5);
pipeline += "2";
});
target.Add(() =>
{
Thread.Sleep(5);
pipeline += "3";
});
Parallel.Invoke(
() => Assert.IsTrue(target.Start()),
() =>
{
Thread.Sleep(100);
target.Stop();
});
}
public void Start_AnyTaskWithException_Exception()
{
var pipeline = "";
var target = new SmartThreadPoolTaskExecutor();
target.Add(() =>
{
throw new Exception("1");
});
target.Add(() =>
{
Thread.Sleep(5);
pipeline += "2";
});
target.Add(() =>
{
Thread.Sleep(5);
pipeline += "3";
});
ExceptionAssert.IsThrowing(new Exception("1"), () =>
{
target.Start();
});
}
public void Stop_Tasks_ShutdownCalled()
{
var pipeline = "";
var target = new SmartThreadPoolTaskExecutor();
target.Timeout = TimeSpan.FromMilliseconds(1000);
target.Add(() =>
{
Thread.Sleep(500);
pipeline += "1";
});
target.Add(() =>
{
Thread.Sleep(500);
pipeline += "2";
});
target.Add(() =>
{
Thread.Sleep(500);
pipeline += "3";
});
Parallel.Invoke(
() => target.Start(),
() =>
{
Thread.Sleep(100);
target.Stop();
});
Assert.IsFalse(target.IsRunning);
}
public void Initialize(IOptimizerConfiguration config, OptimizerFitness fitness)
{
_config = config;
_fitness = fitness;
_executor = new SmartThreadPoolTaskExecutor()
{
MinThreads = 1
};
_executor.MaxThreads = _config.MaxThreads > 0 ? _config.MaxThreads : 8;
}
public void Start_ParallelManyGenerations_Optimization()
{
var taskExecutor = new SmartThreadPoolTaskExecutor();
taskExecutor.MinThreads = 100;
taskExecutor.MaxThreads = 100;
var selection = new EliteSelection();
var crossover = new OnePointCrossover(1);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
FlowAssert.IsAtLeastOneAttemptOk(20, () =>
{
var target = new GeneticAlgorithm(new Population(100, 150, chromosome),
new FitnessStub()
{
SupportsParallel = true
}, selection, crossover, mutation);
target.TaskExecutor = taskExecutor;
Assert.AreEqual(GeneticAlgorithmState.NotStarted, target.State);
Assert.IsFalse(target.IsRunning);
target.Start();
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
Assert.IsTrue(target.Population.CurrentGeneration.Chromosomes.Count >= 100);
Assert.IsTrue(target.Population.CurrentGeneration.Chromosomes.Count <= 150);
Assert.IsNotNull(target.Population.BestChromosome);
Assert.IsTrue(target.Population.BestChromosome.Fitness >= 0.9);
Assert.IsTrue(target.Population.Generations.Count > 0);
});
FlowAssert.IsAtLeastOneAttemptOk(20, () =>
{
var target = new GeneticAlgorithm(new Population(100, 150, chromosome),
new FitnessStub()
{
SupportsParallel = true
}, selection, crossover, mutation);
target.TaskExecutor = taskExecutor;
target.Start();
Assert.IsTrue(target.Population.CurrentGeneration.Chromosomes.Count >= 100);
Assert.IsTrue(target.Population.CurrentGeneration.Chromosomes.Count <= 150);
Assert.IsNotNull(target.Population.BestChromosome);
Assert.IsTrue(target.Population.BestChromosome.Fitness >= 0.9);
Assert.IsTrue(target.Population.Generations.Count > 0);
});
}
public void Start_Timeout_False()
{
var pipeline = "1";
var target = new SmartThreadPoolTaskExecutor();
target.Timeout = TimeSpan.FromMilliseconds(2);
target.Add(() =>
{
Thread.Sleep(10);
pipeline += "2";
});
var actual = target.Start();
Assert.IsFalse(actual);
}
public void Start_Timeout_False()
{
var pipeline = "1";
var target = new SmartThreadPoolTaskExecutor();
target.Timeout = TimeSpan.FromMilliseconds(2);
target.Add(() =>
{
Thread.Sleep(10);
pipeline += "2";
});
var actual = target.Start();
Assert.IsFalse(actual);
}
public void Start_ManyTasks_ParallelExecuted()
{
var pipeline = "";
var target = new SmartThreadPoolTaskExecutor ();
target.Add (() => {
pipeline += "1";
});
target.Add (() => {
Thread.Sleep(10);
pipeline += "2";
});
target.Add (() => {
pipeline += "3";
});
target.Start ();
Assert.AreEqual ("132", pipeline);
}
public void Start_ManyTasks_ParallelExecuted()
{
var pipeline = "";
var target = new SmartThreadPoolTaskExecutor();
target.Add(() => {
pipeline += "1";
});
target.Add(() => {
Thread.Sleep(10);
pipeline += "2";
});
target.Add(() => {
pipeline += "3";
});
target.Start();
Assert.AreEqual("132", pipeline);
}
protected void RunGeneticAlgorithm()
{
try
{
var referencePoint = new ReferencePoint(new GeoPoint3D(_view.StartLongitude, _view.StartLatitude), new Point3D(0, 0));
TrajectoryFitness.ReferencePoint = referencePoint;
TrajectoryFitness.GeoEndPoint = new GeoPoint3D(_view.EndLongitude, _view.EndLatitude);
TrajectoryFitness.OptimiseFuel = !_view.MinimiseNoise;
TrajectoryFitness.TakeoffHeading = _view.TakeoffHeading;
TrajectoryFitness.TakeoffSpeed = _view.TakeoffSpeed;
TrajectoryFitness.trajectories = null;
TrajectoryFitness.Best = int.MinValue;
TrajectoryFitness.PopulationData = new PopulationData2("population.dat", referencePoint);
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new TrajectoryFitness();
var chromosome = new TrajectoryChromosome(TrajectoryChromosome.ChromosomeLength(_view.NumberOfSegments), _view.NumberOfSegments);
var population = new Population(_view.PopulationSize, _view.PopulationSize, chromosome);
ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
var executor = new SmartThreadPoolTaskExecutor();
executor.MinThreads = 1;
executor.MaxThreads = 1;
ga.TaskExecutor = executor;
ga.Termination = new GenerationNumberTermination(_view.NumberOfGenerations);
startTime = DateTime.Now;
ga.TerminationReached += OptimisationCompleted;
ga.GenerationRan += UpdatePercentage;
ga.Start();
}
catch (ThreadAbortException)
{
// ignore it
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
/// <summary>
/// Initializes a new instance of the <see cref="GeneticSharp.Domain.GeneticAlgorithmCCE"/> class.
/// </summary>
/// <param name="species">The species for this CCE</param>
public GeneticAlgorithmCCE(List <CCESpecies> species)
{
Species = species;
TimeEvolving = TimeSpan.Zero;
State = GeneticAlgorithmState.NotStarted;
// TaskExecutorFitMaster = new SmartThreadPoolTaskExecutor () {
// MinThreads = 4,
// MaxThreads = 8
// };
TaskExecutorFitMaster = new LinearTaskExecutor();
TaskExecutorFit = new LinearTaskExecutor();
// TaskExecutorGen = new LinearTaskExecutor ();
// TaskExecutorFit = new SmartThreadPoolTaskExecutor()
// {
// MinThreads = 5,
// MaxThreads = 60
// };
TaskExecutorGen = new SmartThreadPoolTaskExecutor()
{
MinThreads = 4,
MaxThreads = 8
};
}
public void Start()
{
_executor = new SmartThreadPoolTaskExecutor()
{
MinThreads = 1
};
_executor.MaxThreads = _config.MaxThreads > 0 ? _config.MaxThreads : 8;
//create the population
IList <IChromosome> list = new List <IChromosome>();
GeneFactory.Initialize(_config.Genes);
for (int i = 0; i < _config.PopulationSize; i++)
{
list.Add(new Chromosome(true, GeneFactory.Config));
}
int max = _config.PopulationSizeMaximum < _config.PopulationSize ? _config.PopulationSize * 2 : _config.PopulationSizeMaximum;
_population = new PreloadPopulation(_config.PopulationSize, max, list);
_population.GenerationStrategy = new PerformanceGenerationStrategy();
//create the GA itself
var ga = new GeneticAlgorithm(_population, _fitness, new TournamentSelection(),
_config.OnePointCrossover ? new OnePointCrossover() : new TwoPointCrossover(), new UniformMutation(true));
//subscribe to events
ga.GenerationRan += GenerationRan;
ga.TerminationReached += TerminationReached;
ga.TaskExecutor = _executor;
ga.Termination = new OrTermination(new FitnessStagnationTermination(_config.StagnationGenerations), new GenerationNumberTermination(_config.Generations));
ga.Reinsertion = new ElitistReinsertion();
ga.MutationProbability = _config.MutationProbability;
ga.CrossoverProbability = _config.CrossoverProbability;
//run the GA
ga.Start();
}
public void Stop_Tasks_ShutdownCalled()
{
var pipeline = "";
var target = new SmartThreadPoolTaskExecutor();
target.Timeout = TimeSpan.FromMilliseconds(1000);
target.Add(() =>
{
Thread.Sleep(500);
pipeline += "1";
});
target.Add(() =>
{
Thread.Sleep(500);
pipeline += "2";
});
target.Add(() =>
{
Thread.Sleep(500);
pipeline += "3";
});
Parallel.Invoke(
() => target.Start(),
() =>
{
Thread.Sleep(100);
target.Stop();
});
Assert.IsFalse(target.IsRunning);
}
