public void RunNSGAII <S>()
where S : ContinuousVector, new()
{
NSGAII <S> algorithm = new NSGAII <S>(GetProblem());
algorithm.PopulationSize = 100;
algorithm.Initialize();
ClearStatusInfo();
BackgroundWorker worker = new BackgroundWorker();
worker.WorkerReportsProgress = true;
worker.DoWork += (s1, e1) =>
{
while (!algorithm.IsTerminated)
{
algorithm.Evolve();
worker.ReportProgress(0);
}
};
worker.ProgressChanged += (s1, e1) =>
{
txtStatus1.Text = string.Format("Current Generation: {0}", algorithm.CurrentGeneration);
//Console.WriteLine("Number of Dominating Improvements: {0}", algorithm.NumberOfDominatingImprovements);
txtStatus2.Text = string.Format("Size of Archive: {0}", algorithm.NondominatedArchiveSize);
};
worker.RunWorkerCompleted += (s1, e1) =>
{
NondominatedPopulation <S> archive = algorithm.NondominatedArchive;
DisplayParetoFront(archive);
};
worker.RunWorkerAsync();
}
private static void SolveNSGAII()
{
NSGAII nsgaii = new NSGAII();
Console.WriteLine("WA NSGAII Solver Started");
Console.WriteLine("Press ESC to stop");
var res = nsgaii.Solve();
Console.WriteLine("WA NSGAII Solver Finished");
}
static void RunNSGAII()
{
NSGAII <ContinuousVector> algorithm = new NSGAII <ContinuousVector>(new NDNDProblem());
algorithm.PopulationSize = 100;
algorithm.Initialize();
while (!algorithm.IsTerminated)
{
algorithm.Evolve();
Console.WriteLine("Current Generation: {0}", algorithm.CurrentGeneration);
Console.WriteLine("Size of Archive: {0}", algorithm.NondominatedArchiveSize);
}
ContinuousVector finalSolution = algorithm.GlobalBestSolution;
}
/// <summary>
/// Create and return a NeatEvolutionAlgorithm object ready for running the NEAT algorithm/search. Various sub-parts
/// of the algorithm are also constructed and connected up.
/// This overload accepts a pre-built genome population and their associated/parent genome factory.
/// </summary>
public NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory <NeatGenome> genomeFactory, List <NeatGenome> genomeList)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weigth difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategy = new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetric, _parallelOptions);
// Create the evolution algorithm.
NeatEvolutionAlgorithm <NeatGenome> ea = new NeatEvolutionAlgorithm <NeatGenome>(_eaParams, speciationStrategy, _complexityRegulationStrategy);
// Create genome decoder.
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
INoveltyScorer <NeatGenome> noveltyScorer = new TuringNoveltyScorer <NeatGenome>(_noveltySearchParams);
IGeneticDiversityScorer <NeatGenome> geneticDiversityScorer = new GeneticDiversityKnn <NeatGenome>(_neatGenomeParams.ConnectionWeightRange);
IMultiObjectiveScorer multiObjectiveScorer = new NSGAII(_multiObjectiveParams);
_listEvaluator = new MultiObjectiveListEvaluator <NeatGenome, IBlackBox>(
genomeDecoder,
_evaluator,
noveltyScorer,
geneticDiversityScorer,
multiObjectiveScorer,
_multiThreading,
_parallelOptions);
_listEvaluator.MultiObjectiveParams = _multiObjectiveParams;
_listEvaluator.ReportInterval = _logInterval;
NoveltySearchEnabled = _noveltySearchParams?.Enabled ?? false;
MultiObjectiveEnabled = _multiObjectiveParams?.Enabled ?? false;
// Initialize the evolution algorithm.
ea.Initialize(_listEvaluator, genomeFactory, genomeList);
ea.UpdateScheme = new UpdateScheme(1);
// Finished. Return the evolution algorithm
return(ea);
}
private void Button1_Click(object sender, EventArgs e)
{
OpenFileDialog openFileDialog1 = new OpenFileDialog();
openFileDialog1.Title = "Open Matrix File";
openFileDialog1.DefaultExt = "xlsx";
openFileDialog1.Filter = "Excel files (*.xlsx)|*.xlsx";
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
label1.Text = openFileDialog1.FileName;
}
OpenExcel a = new OpenExcel(label1.Text);
featureModel fmO = new featureModel(a.OldMatrix);
PaireSet newPairs = new PaireSet(a.newPairs);
Compare c = new Compare(fmO, newPairs, a.ChangedFeatureList);
FilterTestCase f = new FilterTestCase(c);
for (int i = 0; i < c.NewPairs.Count; i++)
{
listBox1.Items.Add(c.NewPairs[i].Feature1 + "," + c.NewPairs[i].Feature2);
}
listBox2.DataSource = f.RetestableTestCases;
listBox3.DataSource = f.ReUsableTestCases;
listBox4.DataSource = f.ObsoleteTestCases;
listBox5.DataSource = GetPairsList(c.Changed);
listBox6.DataSource = GetPairsList(c.SamePairs);
listBox7.DataSource = GetPairsList(c.RemovedPairs);
int[] rowcol = new int[2];
string[] arg = new string[1];
arg[0] = "Regression";
rowcol[0] = f.Matrix.GetLength(0);
rowcol[1] = f.Matrix.GetLength(1);
NSGAII.Matrix = f.Matrix;
NSGAII.RowCol = rowcol;
NSGAII.Main(arg);
}
private void Button1_Click(object sender, EventArgs e)
{
CleareLabel();
OpenFileDialog openFileDialog1 = new OpenFileDialog();
openFileDialog1.Title = "Open Matrix File";
openFileDialog1.DefaultExt = "xlsx";
openFileDialog1.Filter = "Excel files (*.xlsx)|*.xlsx";
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
FileName = getFileName(openFileDialog1.FileName.Split('\\').Last());
string path = openFileDialog1.FileName;
CurrentFileName = path.Split('\\').Last();
label1.Text = path;
OpenExcel a = new OpenExcel(label1.Text);
fmO = new featureModel(a.OldMatrix);
PaireSet newPairs = new PaireSet(a.newPairs);
compair = new Compare(fmO, newPairs, a.ChangedFeatureList);
filter = new FilterTestCase(compair);
Faults = setFaults(compair.ChangedPairs.Count);
List <string> list1Source = new List <string>();
for (int i = 0; i < compair.NewPairs.Count; i++)
{
list1Source.Add(compair.NewPairs[i].Feature1 + "," + compair.NewPairs[i].Feature2);
}
listBox1.DataSource = list1Source;
listBox2.DataSource = filter.RetestableTestCases;
listBox3.DataSource = filter.ReUsableTestCases;
listBox4.DataSource = filter.ObsoleteTestCases;
listBox5.DataSource = GetPairsList(compair.ChangedPairs);
listBox6.DataSource = GetPairsList(compair.SamePairs);
listBox7.DataSource = GetPairsList(compair.RemovedPairs);
int[] rowcol = new int[2];
string[] arg = new string[1];
arg[0] = "Regression";
rowcol[0] = filter.Matrix.GetLength(0);
rowcol[1] = filter.Matrix.GetLength(1);
NSGAII.Matrix = filter.Matrix;
NSGAII.RowCol = rowcol;
NSGAII.Main(arg);
//JMetalCSharp.Core.SolutionSet s = new JMetalCSharp.Core.SolutionSet();
List <string> fun = new List <string>();
Var = NSGAII.variables;
fun = NSGAII.fun;
//fun.Sort();
listBox8.DataSource = Var;
listBox9.DataSource = SeperateFun(fun);
RC = new computingRandom(fmO, compair, filter, Faults);
double Randomreusability = RC.GetReusability(fmO.Pairs, RC.Variable.ToList());
LRCover.Text = RC.UnCoverage;
LRCost.Text = RC.Cost;
LRFDE.Text = RC.FDE.ToString();
string[] algo = listBox9.Items[0].ToString().Split("\t"[0]);
int[] ourVariable = new int[compair.TestName.Count];
ourVariable = SetOurVariable(filter.removedTest, Var[0]);
computingRandom mr = new computingRandom();
double MyFDE = mr.GetFDE(ourVariable, Faults, compair.ChangedPairs);
double MyReusability = mr.GetReusability(fmO.Pairs, ourVariable.ToList(), compair.ChangedPairs.Count);
//double MyReusability = mr.GetReusability(compair.ChangedPairs, ourVariable.ToList(),compair.ChangedPairs.Count);
Reusability = (MyReusability - Randomreusability) / Randomreusability;
MyFDE = Math.Round(MyFDE, 2);
Reusability = Math.Round(Reusability, 2);
Lcoverage.Text = algo[0];
Lcost.Text = algo[1].Trim();
LFDE.Text = MyFDE.ToString();
//Appearence -------------------------------------------------------------
if (Convert.ToDouble(RC.UnCoverage) > Convert.ToDouble(algo[0]))
{
tick1.Text = "\u2714";
tick1.ForeColor = Color.FromArgb(80, 200, 80);
}
else
{
tick1.Text = "X";
tick1.ForeColor = Color.FromArgb(200, 80, 80);
}
if (Convert.ToDouble(RC.Cost) > Convert.ToDouble(algo[1]))
{
tick2.Text = "\u2714";
tick2.ForeColor = Color.FromArgb(80, 200, 80);
}
else
{
tick2.Text = "X";
tick2.ForeColor = Color.FromArgb(200, 80, 80);
}
if (Convert.ToDouble(RC.FDE) < Convert.ToDouble(MyFDE))
{
tick3.Text = "\u2714";
tick3.ForeColor = Color.FromArgb(80, 200, 80);
}
else
{
tick3.Text = "X";
tick3.ForeColor = Color.FromArgb(200, 80, 80);
}
//Appearence -------------------------------------------------------------
//SaveCompairViaRandom
SaveCompairViaRandom(MyReusability, Randomreusability);
saveResult(path);
int numberoftest = 0;
string variable = Var[0];
for (int i = 0; i < Var[0].Length; i++)
{
if (variable[i].ToString() == "1")
{
numberoftest++;
}
}
//int numberOfSelectedTest = filter.RetestableTestCases.Count;
double newCover = 1 - Convert.ToDouble(algo[0]);
VersionEvaluation Ve = new VersionEvaluation(fmO.Pairs, compair.initialSamePairs, newPairs.PairsList.Count, numberoftest, Faults, compair.ChangedPairs, MyFDE, MyReusability, newCover);
SaveVersionEval SEV = new SaveVersionEval(Ve.M5, Ve.M6, Ve.M7, Ve.M8, CurrentFileName);
SEV.Run();
Dolabel();
}
}
static void Main(string[] args)
{
List <Individuo> solucion = new List <Individuo>();
Seleccion seleccion;
Mutacion mutacion;
Reemplazo reemplazo;
Cruce cruce;
PaisajeAptitud paisaje;
Random rand;
Archivo.Archivo datosDistancia;
Archivo.Archivo datosTiempo;
int opc = 0;
datosDistancia = new Archivo.Archivo("DataSetDistancia.txt", 0);
datosTiempo = new Archivo.Archivo("DataSetTiempo.txt", 0);
double[][] matrizDistacncia, matrizTiempo;
matrizDistacncia = datosDistancia.getMatriz();
matrizTiempo = datosTiempo.getMatriz();
do
{
try
{
int dimensiones, numIteraciones, semilla;
dimensiones = datosDistancia.cantLineas;
//Parametros a afinar
semilla = (new Random()).Next(1, 1001);
numIteraciones = 500;
if (semilla == -1)
{
rand = new Random();
}
else
{
rand = new Random(semilla);
}
NSGAII algoritmo;
algoritmo = new NSGAII(rand);
paisaje = new fxAgenteViajero(opc, matrizDistacncia, matrizTiempo);
cruce = new Combinado(rand, 2);
mutacion = new Intercambio(rand);
seleccion = new SxTorneo(rand, 2);
reemplazo = new DelPeor();
algoritmo.inicializar(cruce, mutacion, seleccion, paisaje, reemplazo, numIteraciones, dimensiones, 0.50);
solucion = algoritmo.ejecutar();
Console.WriteLine("Semilla: " + semilla + " Tamaño poblacion: " + algoritmo.tamañoPoblacion + " Ciudades: " + algoritmo.dimensiones);
/*Console.WriteLine("Mejor individuo");
* solucion[0].mostrar();
* Console.WriteLine("Aptitud: " + solucion[0].getAptitud());*/
foreach (var ind in solucion)
{
Debug.WriteLine(ind.FitnessDistancia + "\t" + ind.FitnessTiempo + "\t" + ind.Rank);
}
}
catch (Exception e) { Console.WriteLine("Error: " + e.Message + "\n" + e.StackTrace); }
Console.Write("Presiona 0 para salir...\nOtra tecla para volver a ejecutar...");
try
{
opc = Int32.Parse(Console.ReadKey(false).KeyChar.ToString());
}
catch (Exception e) { opc = -1; }
//Console.Clear();
} while (opc != 0);
datosDistancia.close();
datosTiempo.close();
}
static void Main()
{
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
Application.Run(new LoadConfig());
// VariabilityModel model = new VariabilityModel("Model1");
// InfluenceModel infModel = new InfluenceModel(model, new NFProperty("Test"));
// BettyFileParser bfp = new BettyFileParser(model, infModel);
String[] file = System.IO.File.ReadAllLines(@"C:\Users\Tom\Desktop\Masterarbeit\SPLConqueror\SPLConqueror\BettyConqueror\FeatureModel0.afm");
// bfp.readConfiguration(file);
//ILArray<float> A = ILMath.tosingle()
//ILArray<float> A = ILMath.tosingle(ILMath.rand(3, 100));
// RDotNet.NativeLibrary.UnmanagedDll.SetDllDirectory(@"C:\Program Files\R\R-3.2.0\bin\i386\R.dll");
engine = REngine.GetInstance();
NumericVector group1 = engine.CreateNumericVector(new double[] { 30.02, 29.99, 30.11, 29.97, 30.01, 29.99 });
engine.SetSymbol("group1", group1);
// e.Evaluate("hist(group1)");
// e.Evaluate("hist(group1)");
Problem problem;
Operator crossover; // Crossover operator
Operator mutation; // Mutation operator
Operator selection; // Selection operator
var logger = Logger.Log;
// var appenders = logger.Logger.Repository.GetAppenders();
// var fileAppender = appenders[0] as log4net.Appender.FileAppender;
// fileAppender.File = "NSGAII.log";
//.ActivateOptions();
Dictionary <string, object> parameters; // Operator parameters
QualityIndicator indicators; // Object to get quality indicators
problem = new GeneratorProblem("Real", 30);
NSGAII algorithm = new NSGAII(problem);
indicators = null;
algorithm.SetInputParameter("populationSize", 200);
algorithm.SetInputParameter("maxEvaluations", 25000);
// Mutation and Crossover for Real codification
parameters = new Dictionary <string, object>();
parameters.Add("probability", 0.9);
parameters.Add("distributionIndex", 20.0);
crossover = CrossoverFactory.GetCrossoverOperator("SBXCrossover", parameters);
parameters = new Dictionary <string, object>();
parameters.Add("probability", 1.0 / problem.NumberOfVariables);
parameters.Add("distributionIndex", 20.0);
mutation = MutationFactory.GetMutationOperator("PolynomialMutation", parameters);
// Selection Operator
parameters = null;
selection = SelectionFactory.GetSelectionOperator("BinaryTournament2", parameters);
// Add the operators to the algorithm
algorithm.AddOperator("crossover", crossover);
algorithm.AddOperator("mutation", mutation);
algorithm.AddOperator("selection", selection);
// Add the indicator object to the algorithm
algorithm.SetInputParameter("indicators", indicators);
// Execute the Algorithm
long initTime = Environment.TickCount;
//SolutionSet population = algorithm.Execute();
long estimatedTime = Environment.TickCount - initTime;
logger.Info("Variables values have been writen to file VAR");
//population.PrintVariablesToFile("VAR");
logger.Info("Objectives values have been writen to file FUN");
// population.PrintObjectivesToFile("FUN");
Console.WriteLine("Time: " + estimatedTime);
Console.ReadLine();
//Application.Run(new Form1(e, group1, new double[] { 30.02, 29.99, 30.11, 29.97, 30.01, 29.99, 1.3, 1.7, 9.4, 11.1 }));
}
public void RunSimulation(object sender, EventArgs e)
{
var options = CreateAlgorithmOptions();
var problem = new ConfigurationProblem();
var algorithm = new NSGAII(problem);
algorithm.SetInputParameter("populationSize", options.NumberOfCromozoms);
algorithm.SetInputParameter("maxEvaluations", options.NumberOfCromozoms * options.NumberOfGenerations);
// Mutation and Crossover for Real codification
var parameters = new Dictionary <string, object>();
parameters.Add(ConfigurationCrossOver.CrossOverPercentageKey, options.CrossOverPercentage);
var crossover = new ConfigurationCrossOver(parameters); // Nobody cares about the factory
parameters = new Dictionary <string, object>();
parameters.Add(ConfigurationMutation.MutationPercentageKey, options.MutationPercentage);
var mutation = new ConfigurationMutation(parameters); // Nobody cares about the factory.
// Selection Operator
parameters = null;
var selection = new BinaryTournament2(parameters); // Nobody cares about the factory.
// Add the operators to the algorithm
algorithm.AddOperator("crossover", crossover);
algorithm.AddOperator("mutation", mutation);
algorithm.AddOperator("selection", selection);
// Add the indicator object to the algorithm
// nope
//algorithm.SetInputParameter("indicators", null);
var solution = algorithm.Execute();
var solutionsList = solution.SolutionsList;
var output = "";
using (var writer = new System.IO.StreamWriter("output.txt")) {
foreach (var sol in solutionsList)
{
var line = sol.Objective [0] + "\t" + sol.Objective [1];
output += line;
output += "\n";
writer.WriteLine(line);
}
output += "\n";
output += "\n";
writer.WriteLine();
writer.WriteLine();
var rank = new Ranking(solution);
var n = rank.GetNumberOfSubfronts();
for (var i = 0; i < n; i++)
{
var f = rank.GetSubfront(i);
foreach (var sol in f.SolutionsList)
{
var line = sol.Objective [0] + "\t" + sol.Objective [1];
output += line;
output += "\n";
writer.WriteLine(line);
}
output += "\n";
output += "\n";
writer.WriteLine();
writer.WriteLine();
}
}
resultsText.Buffer.Clear();
resultsText.Buffer.Text = output;
}
