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);
}
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);
}
}
