Index: trunk/sources/HeuristicLab.Tests/HeuristicLab-3.3/Samples/GPMultiplexerSampleTest.cs
===================================================================
--- trunk/sources/HeuristicLab.Tests/HeuristicLab-3.3/Samples/GPMultiplexerSampleTest.cs	(revision 11051)
+++ trunk/sources/HeuristicLab.Tests/HeuristicLab-3.3/Samples/GPMultiplexerSampleTest.cs	(revision 11051)
@@ -0,0 +1,115 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.IO;
+using HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm;
+using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
+using HeuristicLab.Persistence.Default.Xml;
+using HeuristicLab.Problems.DataAnalysis.Symbolic;
+using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
+using HeuristicLab.Problems.Instances.DataAnalysis;
+using HeuristicLab.Selection;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+
+namespace HeuristicLab.Tests {
+  [TestClass]
+  public class GPMultiplexerSampleTest {
+    private const string samplesDirectory = SamplesUtils.Directory;
+    [ClassInitialize]
+    public static void MyClassInitialize(TestContext testContext) {
+      if (!Directory.Exists(samplesDirectory))
+        Directory.CreateDirectory(samplesDirectory);
+    }
+
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateGpMultiplexerSampleTest() {
+      var ga = CreateGpMultiplexerSample();
+      var path = Path.Combine(samplesDirectory, "GP_Multiplexer.hl");
+      XmlGenerator.Serialize(ga, path);
+    }
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunGpMultiplexerSampleTest() {
+      var osga = CreateGpMultiplexerSample();
+      osga.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(osga);
+
+      Assert.AreEqual(0.125, SamplesUtils.GetDoubleResult(osga, "BestQuality"), 1E-8);
+      Assert.AreEqual(0.237275390625, SamplesUtils.GetDoubleResult(osga, "CurrentAverageQuality"), 1E-8);
+      Assert.AreEqual(1.181640625, SamplesUtils.GetDoubleResult(osga, "CurrentWorstQuality"), 1E-8);
+      Assert.AreEqual(105500, SamplesUtils.GetIntResult(osga, "EvaluatedSolutions"));
+    }
+
+    public static OffspringSelectionGeneticAlgorithm CreateGpMultiplexerSample() {
+      var instanceProvider = new RegressionCSVInstanceProvider();
+      var regressionImportType = new RegressionImportType();
+      regressionImportType.TargetVariable = "output";
+      regressionImportType.TrainingPercentage = 100;
+      var dataAnalysisCSVFormat = new DataAnalysisCSVFormat();
+      dataAnalysisCSVFormat.Separator = ',';
+      dataAnalysisCSVFormat.VariableNamesAvailable = true;
+
+      var problemData = instanceProvider.ImportData(@"Test Resources\Multiplexer11.csv", regressionImportType, dataAnalysisCSVFormat);
+      problemData.Name = "11-Multiplexer";
+
+      var problem = new SymbolicRegressionSingleObjectiveProblem();
+      problem.Name = "11-Multiplexer Problem";
+      problem.ProblemData = problemData;
+      problem.MaximumSymbolicExpressionTreeLength.Value = 50;
+      problem.MaximumSymbolicExpressionTreeDepth.Value = 50;
+      problem.EvaluatorParameter.Value = new SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator();
+      problem.ApplyLinearScaling.Value = false;
+
+
+      var grammar = new FullFunctionalExpressionGrammar();
+      problem.SymbolicExpressionTreeGrammar = grammar;
+      foreach (var symbol in grammar.Symbols) {
+        if (symbol is ProgramRootSymbol) symbol.Enabled = true;
+        else if (symbol is StartSymbol) symbol.Enabled = true;
+        else if (symbol is IfThenElse) symbol.Enabled = true;
+        else if (symbol is And) symbol.Enabled = true;
+        else if (symbol is Or) symbol.Enabled = true;
+        else if (symbol is Xor) symbol.Enabled = true;
+        else if (symbol.GetType() == typeof(Variable)) {
+          //necessary as there are multiple classes derived from Variable (e.g., VariableCondition)
+          symbol.Enabled = true;
+          var variableSymbol = (Variable)symbol;
+          variableSymbol.MultiplicativeWeightManipulatorSigma = 0.0;
+          variableSymbol.WeightManipulatorSigma = 0.0;
+          variableSymbol.WeightSigma = 0.0;
+        } else symbol.Enabled = false;
+      }
+
+      var osga = new OffspringSelectionGeneticAlgorithm();
+      osga.Name = "Genetic Programming - Multiplexer 11 problem";
+      osga.Description = "A genetic programming algorithm that solves the 11-bit multiplexer problem.";
+      osga.Problem = problem;
+      SamplesUtils.ConfigureOsGeneticAlgorithmParameters<GenderSpecificSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>
+        (osga, popSize: 100, elites: 1, maxGens: 50, mutationRate: 0.25);
+      osga.MaximumSelectionPressure.Value = 200;
+      return osga;
+
+    }
+  }
+}
Index: trunk/sources/HeuristicLab.Tests/HeuristicLab-3.3/Samples/SamplesTest.cs
===================================================================
--- trunk/sources/HeuristicLab.Tests/HeuristicLab-3.3/Samples/SamplesTest.cs	(revision 11051)
+++ trunk/sources/HeuristicLab.Tests/HeuristicLab-3.3/Samples/SamplesTest.cs	(revision 11051)
@@ -0,0 +1,1283 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.IO;
+using System.Linq;
+using System.Threading;
+using HeuristicLab.Algorithms.DataAnalysis;
+using HeuristicLab.Algorithms.EvolutionStrategy;
+using HeuristicLab.Algorithms.GeneticAlgorithm;
+using HeuristicLab.Algorithms.LocalSearch;
+using HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm;
+using HeuristicLab.Algorithms.ParticleSwarmOptimization;
+using HeuristicLab.Algorithms.RAPGA;
+using HeuristicLab.Algorithms.ScatterSearch;
+using HeuristicLab.Algorithms.SimulatedAnnealing;
+using HeuristicLab.Algorithms.TabuSearch;
+using HeuristicLab.Algorithms.VariableNeighborhoodSearch;
+using HeuristicLab.Data;
+using HeuristicLab.Encodings.BinaryVectorEncoding;
+using HeuristicLab.Encodings.PermutationEncoding;
+using HeuristicLab.Encodings.RealVectorEncoding;
+using HeuristicLab.Encodings.ScheduleEncoding.JobSequenceMatrix;
+using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
+using HeuristicLab.Optimization;
+using HeuristicLab.Optimization.Operators;
+using HeuristicLab.Persistence.Default.Xml;
+using HeuristicLab.Problems.ArtificialAnt;
+using HeuristicLab.Problems.DataAnalysis;
+using HeuristicLab.Problems.DataAnalysis.Symbolic;
+using HeuristicLab.Problems.DataAnalysis.Symbolic.Classification;
+using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
+using HeuristicLab.Problems.Instances;
+using HeuristicLab.Problems.Instances.DataAnalysis;
+using HeuristicLab.Problems.Instances.TSPLIB;
+using HeuristicLab.Problems.Instances.VehicleRouting;
+using HeuristicLab.Problems.Knapsack;
+using HeuristicLab.Problems.Scheduling;
+using HeuristicLab.Problems.TestFunctions;
+using HeuristicLab.Problems.TravelingSalesman;
+using HeuristicLab.Problems.VehicleRouting;
+using HeuristicLab.Problems.VehicleRouting.Encodings.General;
+using HeuristicLab.Problems.VehicleRouting.Encodings.Potvin;
+using HeuristicLab.Problems.VehicleRouting.ProblemInstances;
+using HeuristicLab.Selection;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using StdDevStrategyVectorCreator = HeuristicLab.Encodings.RealVectorEncoding.StdDevStrategyVectorCreator;
+using StdDevStrategyVectorCrossover = HeuristicLab.Encodings.RealVectorEncoding.StdDevStrategyVectorCrossover;
+using StdDevStrategyVectorManipulator = HeuristicLab.Encodings.RealVectorEncoding.StdDevStrategyVectorManipulator;
+
+
+namespace HeuristicLab.Tests {
+  [TestClass]
+  public class SamplesTest {
+    private const string samplesDirectory = SamplesUtils.Directory;
+
+    [ClassInitialize]
+    public static void MyClassInitialize(TestContext testContext) {
+      if (!Directory.Exists(samplesDirectory))
+        Directory.CreateDirectory(samplesDirectory);
+    }
+
+    #region GA
+    #region TSP
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateGaTspSampleTest() {
+      var ga = CreateGaTspSample();
+      XmlGenerator.Serialize(ga, @"Samples\GA_TSP.hl");
+    }
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunGaTspSampleTest() {
+      var ga = CreateGaTspSample();
+      ga.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(ga);
+      Assert.AreEqual(12332, SamplesUtils.GetDoubleResult(ga, "BestQuality"));
+      Assert.AreEqual(13123.2, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"));
+      Assert.AreEqual(14538, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"));
+      Assert.AreEqual(99100, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
+    }
+
+    private GeneticAlgorithm CreateGaTspSample() {
+      GeneticAlgorithm ga = new GeneticAlgorithm();
+      #region Problem Configuration
+      var provider = new TSPLIBTSPInstanceProvider();
+      var instance = provider.GetDataDescriptors().Where(x => x.Name == "ch130").Single();
+      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
+      tspProblem.Load(provider.LoadData(instance));
+      tspProblem.UseDistanceMatrix.Value = true;
+      #endregion
+      #region Algorithm Configuration
+      ga.Name = "Genetic Algorithm - TSP";
+      ga.Description = "A genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
+      ga.Problem = tspProblem;
+      SamplesUtils.ConfigureGeneticAlgorithmParameters<ProportionalSelector, OrderCrossover2, InversionManipulator>(
+        ga, 100, 1, 1000, 0.05);
+      #endregion
+      return ga;
+    }
+    #endregion
+    #region VRP
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateGaVrpSampleTest() {
+      var ga = CreateGaVrpSample();
+      XmlGenerator.Serialize(ga, @"Samples\GA_VRP.hl");
+    }
+
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunGaVrpSampleTest() {
+      var ga = CreateGaVrpSample();
+      ga.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(ga);
+      Assert.AreEqual(1828.9368669428338, SamplesUtils.GetDoubleResult(ga, "BestQuality"));
+      Assert.AreEqual(1830.1444308908331, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"));
+      Assert.AreEqual(1871.7128510304112, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"));
+      Assert.AreEqual(99100, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
+    }
+
+    private GeneticAlgorithm CreateGaVrpSample() {
+      GeneticAlgorithm ga = new GeneticAlgorithm();
+      #region Problem Configuration
+      VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
+
+      SolomonFormatInstanceProvider instanceProvider = new SolomonInstanceProvider();
+      CVRPTWData data = instanceProvider.Import(@"Test Resources\C101.txt", @"Test Resources\C101.opt.txt") as CVRPTWData;
+      vrpProblem.Load(data);
+      vrpProblem.Name = "C101 VRP (imported from Solomon)";
+      vrpProblem.Description = "Represents a Vehicle Routing Problem.";
+      CVRPTWProblemInstance instance = vrpProblem.ProblemInstance as CVRPTWProblemInstance;
+      instance.DistanceFactor.Value = 1;
+      instance.FleetUsageFactor.Value = 100;
+      instance.OverloadPenalty.Value = 100;
+      instance.TardinessPenalty.Value = 100;
+      instance.TimeFactor.Value = 0;
+      vrpProblem.MaximizationParameter.Value.Value = false;
+      instance.UseDistanceMatrix.Value = true;
+      instance.Vehicles.Value = 25;
+      #endregion
+      #region Algorithm Configuration
+      ga.Name = "Genetic Algorithm - VRP";
+      ga.Description = "A genetic algorithm which solves the \"C101\" vehicle routing problem (imported from Solomon)";
+      ga.Problem = vrpProblem;
+      SamplesUtils.ConfigureGeneticAlgorithmParameters<TournamentSelector, MultiVRPSolutionCrossover, MultiVRPSolutionManipulator>(
+        ga, 100, 1, 1000, 0.05, 3);
+
+      var xOver = (MultiVRPSolutionCrossover)ga.Crossover;
+      foreach (var op in xOver.Operators) {
+        xOver.Operators.SetItemCheckedState(op, false);
+      }
+      xOver.Operators.SetItemCheckedState(xOver.Operators
+        .OfType<PotvinRouteBasedCrossover>()
+        .Single(), true);
+      xOver.Operators.SetItemCheckedState(xOver.Operators
+        .OfType<PotvinSequenceBasedCrossover>()
+        .Single(), true);
+
+      var manipulator = (MultiVRPSolutionManipulator)ga.Mutator;
+      foreach (var op in manipulator.Operators) {
+        manipulator.Operators.SetItemCheckedState(op, false);
+      }
+      manipulator.Operators.SetItemCheckedState(manipulator.Operators
+        .OfType<PotvinOneLevelExchangeMainpulator>()
+        .Single(), true);
+      manipulator.Operators.SetItemCheckedState(manipulator.Operators
+        .OfType<PotvinTwoLevelExchangeManipulator>()
+        .Single(), true);
+      #endregion
+      return ga;
+    }
+    #endregion
+    #region ArtificialAnt
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateGpArtificialAntSampleTest() {
+      var ga = CreateGpArtificialAntSample();
+      XmlGenerator.Serialize(ga, @"Samples\SGP_SantaFe.hl");
+    }
+
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunGpArtificialAntSampleTest() {
+      var ga = CreateGpArtificialAntSample();
+      ga.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(ga);
+      Assert.AreEqual(81, SamplesUtils.GetDoubleResult(ga, "BestQuality"));
+      Assert.AreEqual(48.19, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"));
+      Assert.AreEqual(0, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"));
+      Assert.AreEqual(50950, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
+    }
+
+    public GeneticAlgorithm CreateGpArtificialAntSample() {
+      GeneticAlgorithm ga = new GeneticAlgorithm();
+      #region Problem Configuration
+      ArtificialAntProblem antProblem = new ArtificialAntProblem();
+      antProblem.BestKnownQuality.Value = 89;
+      antProblem.MaxExpressionDepth.Value = 10;
+      antProblem.MaxExpressionLength.Value = 100;
+      antProblem.MaxFunctionArguments.Value = 3;
+      antProblem.MaxFunctionDefinitions.Value = 3;
+      antProblem.MaxTimeSteps.Value = 600;
+      #endregion
+      #region Algorithm Configuration
+      ga.Name = "Genetic Programming - Artificial Ant";
+      ga.Description = "A standard genetic programming algorithm to solve the artificial ant problem (Santa-Fe trail)";
+      ga.Problem = antProblem;
+      SamplesUtils.ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeArchitectureManipulator>(
+        ga, 1000, 1, 50, 0.15, 5);
+      var mutator = (MultiSymbolicExpressionTreeArchitectureManipulator)ga.Mutator;
+      mutator.Operators.SetItemCheckedState(mutator.Operators
+        .OfType<FullTreeShaker>()
+        .Single(), false);
+      mutator.Operators.SetItemCheckedState(mutator.Operators
+        .OfType<OnePointShaker>()
+        .Single(), false);
+      mutator.Operators.SetItemCheckedState(mutator.Operators
+        .OfType<ArgumentDeleter>()
+        .Single(), false);
+      mutator.Operators.SetItemCheckedState(mutator.Operators
+        .OfType<SubroutineDeleter>()
+        .Single(), false);
+      #endregion
+      return ga;
+    }
+    #endregion
+    #region Symbolic Regression
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateGpSymbolicRegressionSampleTest() {
+      var ga = CreateGpSymbolicRegressionSample();
+      XmlGenerator.Serialize(ga, @"Samples\SGP_SymbReg.hl");
+    }
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunGpSymbolicRegressionSampleTest() {
+      var ga = CreateGpSymbolicRegressionSample();
+      ga.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(ga);
+      Assert.AreEqual(0.858344291534625, SamplesUtils.GetDoubleResult(ga, "BestQuality"), 1E-8);
+      Assert.AreEqual(0.56758466520692641, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"), 1E-8);
+      Assert.AreEqual(0, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"), 1E-8);
+      Assert.AreEqual(50950, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
+      var bestTrainingSolution = (IRegressionSolution)ga.Results["Best training solution"].Value;
+      Assert.AreEqual(0.85504801557844745, bestTrainingSolution.TrainingRSquared, 1E-8);
+      Assert.AreEqual(0.86259381948647817, bestTrainingSolution.TestRSquared, 1E-8);
+      var bestValidationSolution = (IRegressionSolution)ga.Results["Best validation solution"].Value;
+      Assert.AreEqual(0.84854338315539746, bestValidationSolution.TrainingRSquared, 1E-8);
+      Assert.AreEqual(0.8662813452656678, bestValidationSolution.TestRSquared, 1E-8);
+    }
+
+    private GeneticAlgorithm CreateGpSymbolicRegressionSample() {
+      GeneticAlgorithm ga = new GeneticAlgorithm();
+      #region Problem Configuration
+      SymbolicRegressionSingleObjectiveProblem symbRegProblem = new SymbolicRegressionSingleObjectiveProblem();
+      symbRegProblem.Name = "Tower Symbolic Regression Problem";
+      symbRegProblem.Description = "Tower Dataset (downloaded from: http://www.symbolicregression.com/?q=towerProblem)";
+      RegressionRealWorldInstanceProvider provider = new RegressionRealWorldInstanceProvider();
+      var instance = provider.GetDataDescriptors().Where(x => x.Name.Equals("Tower")).Single();
+      var towerProblemData = (RegressionProblemData)provider.LoadData(instance);
+      towerProblemData.TargetVariableParameter.Value = towerProblemData.TargetVariableParameter.ValidValues
+        .First(v => v.Value == "towerResponse");
+      towerProblemData.InputVariables.SetItemCheckedState(
+        towerProblemData.InputVariables.Single(x => x.Value == "x1"), true);
+      towerProblemData.InputVariables.SetItemCheckedState(
+        towerProblemData.InputVariables.Single(x => x.Value == "x7"), false);
+      towerProblemData.InputVariables.SetItemCheckedState(
+        towerProblemData.InputVariables.Single(x => x.Value == "x11"), false);
+      towerProblemData.InputVariables.SetItemCheckedState(
+        towerProblemData.InputVariables.Single(x => x.Value == "x16"), false);
+      towerProblemData.InputVariables.SetItemCheckedState(
+        towerProblemData.InputVariables.Single(x => x.Value == "x21"), false);
+      towerProblemData.InputVariables.SetItemCheckedState(
+        towerProblemData.InputVariables.Single(x => x.Value == "x25"), false);
+      towerProblemData.InputVariables.SetItemCheckedState(
+        towerProblemData.InputVariables.Single(x => x.Value == "towerResponse"), false);
+      towerProblemData.TrainingPartition.Start = 0;
+      towerProblemData.TrainingPartition.End = 3136;
+      towerProblemData.TestPartition.Start = 3136;
+      towerProblemData.TestPartition.End = 4999;
+      towerProblemData.Name = "Data imported from towerData.txt";
+      towerProblemData.Description = "Chemical concentration at top of distillation tower, dataset downloaded from: http://vanillamodeling.com/realproblems.html, best R² achieved with nu-SVR = 0.97";
+      symbRegProblem.ProblemData = towerProblemData;
+
+      // configure grammar
+      var grammar = new TypeCoherentExpressionGrammar();
+      grammar.ConfigureAsDefaultRegressionGrammar();
+      grammar.Symbols.OfType<VariableCondition>().Single().InitialFrequency = 0.0;
+      var varSymbol = grammar.Symbols.OfType<Variable>().Where(x => !(x is LaggedVariable)).Single();
+      varSymbol.WeightMu = 1.0;
+      varSymbol.WeightSigma = 1.0;
+      varSymbol.WeightManipulatorMu = 0.0;
+      varSymbol.WeightManipulatorSigma = 0.05;
+      varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
+      var constSymbol = grammar.Symbols.OfType<Constant>().Single();
+      constSymbol.MaxValue = 20;
+      constSymbol.MinValue = -20;
+      constSymbol.ManipulatorMu = 0.0;
+      constSymbol.ManipulatorSigma = 1;
+      constSymbol.MultiplicativeManipulatorSigma = 0.03;
+      symbRegProblem.SymbolicExpressionTreeGrammar = grammar;
+
+      // configure remaining problem parameters
+      symbRegProblem.BestKnownQuality.Value = 0.97;
+      symbRegProblem.FitnessCalculationPartition.Start = 0;
+      symbRegProblem.FitnessCalculationPartition.End = 2300;
+      symbRegProblem.ValidationPartition.Start = 2300;
+      symbRegProblem.ValidationPartition.End = 3136;
+      symbRegProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
+      symbRegProblem.MaximumSymbolicExpressionTreeLength.Value = 150;
+      symbRegProblem.MaximumSymbolicExpressionTreeDepth.Value = 12;
+      symbRegProblem.MaximumFunctionDefinitions.Value = 0;
+      symbRegProblem.MaximumFunctionArguments.Value = 0;
+
+      symbRegProblem.EvaluatorParameter.Value = new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator();
+      #endregion
+      #region Algorithm Configuration
+      ga.Problem = symbRegProblem;
+      ga.Name = "Genetic Programming - Symbolic Regression";
+      ga.Description = "A standard genetic programming algorithm to solve a symbolic regression problem (tower dataset)";
+      SamplesUtils.ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
+        ga, 1000, 1, 50, 0.15, 5);
+      var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
+      mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
+      mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
+
+      ga.Analyzer.Operators.SetItemCheckedState(
+        ga.Analyzer.Operators
+        .OfType<SymbolicRegressionSingleObjectiveOverfittingAnalyzer>()
+        .Single(), false);
+      ga.Analyzer.Operators.SetItemCheckedState(
+        ga.Analyzer.Operators
+        .OfType<SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
+        .First(), false);
+      #endregion
+      return ga;
+    }
+    #endregion
+    #region Symbolic Classification
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateGpSymbolicClassificationSampleTest() {
+      var ga = CreateGpSymbolicClassificationSample();
+      XmlGenerator.Serialize(ga, @"Samples\SGP_SymbClass.hl");
+    }
+
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunGpSymbolicClassificationSampleTest() {
+      var ga = CreateGpSymbolicClassificationSample();
+      ga.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(ga);
+      Assert.AreEqual(0.141880203907627, SamplesUtils.GetDoubleResult(ga, "BestQuality"), 1E-8);
+      Assert.AreEqual(4.3246992327753295, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"), 1E-8);
+      Assert.AreEqual(100.62175156249987, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"), 1E-8);
+      Assert.AreEqual(100900, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
+      var bestTrainingSolution = (IClassificationSolution)ga.Results["Best training solution"].Value;
+      Assert.AreEqual(0.80875, bestTrainingSolution.TrainingAccuracy, 1E-8);
+      Assert.AreEqual(0.795031055900621, bestTrainingSolution.TestAccuracy, 1E-8);
+      var bestValidationSolution = (IClassificationSolution)ga.Results["Best validation solution"].Value;
+      Assert.AreEqual(0.81375, bestValidationSolution.TrainingAccuracy, 1E-8);
+      Assert.AreEqual(0.788819875776398, bestValidationSolution.TestAccuracy, 1E-8);
+    }
+
+    private GeneticAlgorithm CreateGpSymbolicClassificationSample() {
+      GeneticAlgorithm ga = new GeneticAlgorithm();
+      #region Problem Configuration
+      SymbolicClassificationSingleObjectiveProblem symbClassProblem = new SymbolicClassificationSingleObjectiveProblem();
+      symbClassProblem.Name = "Mammography Classification Problem";
+      symbClassProblem.Description = "Mammography dataset imported from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass)";
+      UCIInstanceProvider provider = new UCIInstanceProvider();
+      var instance = provider.GetDataDescriptors().Where(x => x.Name.Equals("Mammography, M. Elter, 2007")).Single();
+      var mammoData = (ClassificationProblemData)provider.LoadData(instance);
+      mammoData.TargetVariableParameter.Value = mammoData.TargetVariableParameter.ValidValues
+        .First(v => v.Value == "Severity");
+      mammoData.InputVariables.SetItemCheckedState(
+        mammoData.InputVariables.Single(x => x.Value == "BI-RADS"), false);
+      mammoData.InputVariables.SetItemCheckedState(
+        mammoData.InputVariables.Single(x => x.Value == "Age"), true);
+      mammoData.InputVariables.SetItemCheckedState(
+        mammoData.InputVariables.Single(x => x.Value == "Shape"), true);
+      mammoData.InputVariables.SetItemCheckedState(
+        mammoData.InputVariables.Single(x => x.Value == "Margin"), true);
+      mammoData.InputVariables.SetItemCheckedState(
+        mammoData.InputVariables.Single(x => x.Value == "Density"), true);
+      mammoData.InputVariables.SetItemCheckedState(
+        mammoData.InputVariables.Single(x => x.Value == "Severity"), false);
+      mammoData.TrainingPartition.Start = 0;
+      mammoData.TrainingPartition.End = 800;
+      mammoData.TestPartition.Start = 800;
+      mammoData.TestPartition.End = 961;
+      mammoData.Name = "Data imported from mammographic_masses.csv";
+      mammoData.Description = "Original dataset: http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass, missing values have been replaced with median values.";
+      symbClassProblem.ProblemData = mammoData;
+
+      // configure grammar
+      var grammar = new TypeCoherentExpressionGrammar();
+      grammar.ConfigureAsDefaultClassificationGrammar();
+      grammar.Symbols.OfType<VariableCondition>().Single().Enabled = false;
+      var varSymbol = grammar.Symbols.OfType<Variable>().Where(x => !(x is LaggedVariable)).Single();
+      varSymbol.WeightMu = 1.0;
+      varSymbol.WeightSigma = 1.0;
+      varSymbol.WeightManipulatorMu = 0.0;
+      varSymbol.WeightManipulatorSigma = 0.05;
+      varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
+      var constSymbol = grammar.Symbols.OfType<Constant>().Single();
+      constSymbol.MaxValue = 20;
+      constSymbol.MinValue = -20;
+      constSymbol.ManipulatorMu = 0.0;
+      constSymbol.ManipulatorSigma = 1;
+      constSymbol.MultiplicativeManipulatorSigma = 0.03;
+      symbClassProblem.SymbolicExpressionTreeGrammar = grammar;
+
+      // configure remaining problem parameters
+      symbClassProblem.BestKnownQuality.Value = 0.0;
+      symbClassProblem.FitnessCalculationPartition.Start = 0;
+      symbClassProblem.FitnessCalculationPartition.End = 400;
+      symbClassProblem.ValidationPartition.Start = 400;
+      symbClassProblem.ValidationPartition.End = 800;
+      symbClassProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
+      symbClassProblem.MaximumSymbolicExpressionTreeLength.Value = 100;
+      symbClassProblem.MaximumSymbolicExpressionTreeDepth.Value = 10;
+      symbClassProblem.MaximumFunctionDefinitions.Value = 0;
+      symbClassProblem.MaximumFunctionArguments.Value = 0;
+      symbClassProblem.EvaluatorParameter.Value = new SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator();
+      #endregion
+      #region Algorithm Configuration
+      ga.Problem = symbClassProblem;
+      ga.Name = "Genetic Programming - Symbolic Classification";
+      ga.Description = "A standard genetic programming algorithm to solve a classification problem (Mammographic+Mass dataset)";
+      SamplesUtils.ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
+        ga, 1000, 1, 100, 0.15, 5
+        );
+
+      var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
+      mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
+      mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
+
+      ga.Analyzer.Operators.SetItemCheckedState(
+        ga.Analyzer.Operators
+        .OfType<SymbolicClassificationSingleObjectiveOverfittingAnalyzer>()
+        .Single(), false);
+      ga.Analyzer.Operators.SetItemCheckedState(
+        ga.Analyzer.Operators
+        .OfType<SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
+        .First(), false);
+      #endregion
+      return ga;
+    }
+    #endregion
+    #region LawnMower
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunGpLawnMowerSampleTest() {
+      var ga = CreateGpLawnMowerSample();
+      ga.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(ga);
+    }
+
+    public GeneticAlgorithm CreateGpLawnMowerSample() {
+      GeneticAlgorithm ga = new GeneticAlgorithm();
+      #region Problem Configuration
+      var problem = new HeuristicLab.Problems.LawnMower.Problem();
+      #endregion
+      #region Algorithm Configuration
+      ga.Name = "Genetic Programming - Lawn Mower";
+      ga.Description = "A standard genetic programming algorithm to solve the lawn mower problem";
+      ga.Problem = problem;
+      SamplesUtils.ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeArchitectureManipulator>(
+        ga, 1000, 1, 50, 0.25, 5);
+      var mutator = (MultiSymbolicExpressionTreeArchitectureManipulator)ga.Mutator;
+      mutator.Operators.SetItemCheckedState(mutator.Operators
+        .OfType<OnePointShaker>()
+        .Single(), false);
+      #endregion
+      return ga;
+    }
+    #endregion
+    #endregion
+
+    #region ES
+    #region Griewank
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateEsGriewankSampleTest() {
+      var es = CreateEsGriewankSample();
+      XmlGenerator.Serialize(es, @"Samples\ES_Griewank.hl");
+    }
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunEsGriewankSampleTest() {
+      var es = CreateEsGriewankSample();
+      es.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(es);
+      Assert.AreEqual(0, SamplesUtils.GetDoubleResult(es, "BestQuality"));
+      Assert.AreEqual(0, SamplesUtils.GetDoubleResult(es, "CurrentAverageQuality"));
+      Assert.AreEqual(0, SamplesUtils.GetDoubleResult(es, "CurrentWorstQuality"));
+      Assert.AreEqual(100020, SamplesUtils.GetIntResult(es, "EvaluatedSolutions"));
+    }
+
+    private EvolutionStrategy CreateEsGriewankSample() {
+      EvolutionStrategy es = new EvolutionStrategy();
+      #region Problem Configuration
+      SingleObjectiveTestFunctionProblem problem = new SingleObjectiveTestFunctionProblem();
+
+      problem.ProblemSize.Value = 10;
+      problem.EvaluatorParameter.Value = new GriewankEvaluator();
+      problem.SolutionCreatorParameter.Value = new UniformRandomRealVectorCreator();
+      problem.Maximization.Value = false;
+      problem.Bounds = new DoubleMatrix(new double[,] { { -600, 600 } });
+      problem.BestKnownQuality.Value = 0;
+      problem.BestKnownSolutionParameter.Value = new RealVector(10);
+      problem.Name = "Single Objective Test Function";
+      problem.Description = "Test function with real valued inputs and a single objective.";
+      #endregion
+      #region Algorithm Configuration
+      es.Name = "Evolution Strategy - Griewank";
+      es.Description = "An evolution strategy which solves the 10-dimensional Griewank test function";
+      es.Problem = problem;
+      SamplesUtils.ConfigureEvolutionStrategyParameters<AverageCrossover, NormalAllPositionsManipulator,
+        StdDevStrategyVectorCreator, StdDevStrategyVectorCrossover, StdDevStrategyVectorManipulator>(
+        es, 20, 500, 2, 200, false);
+
+      StdDevStrategyVectorCreator strategyCreator = (StdDevStrategyVectorCreator)es.StrategyParameterCreator;
+      strategyCreator.BoundsParameter.Value = new DoubleMatrix(new double[,] { { 1, 20 } });
+
+      StdDevStrategyVectorManipulator strategyManipulator = (StdDevStrategyVectorManipulator)es.StrategyParameterManipulator;
+      strategyManipulator.BoundsParameter.Value = new DoubleMatrix(new double[,] { { 1E-12, 30 } });
+      strategyManipulator.GeneralLearningRateParameter.Value = new DoubleValue(0.22360679774997896);
+      strategyManipulator.LearningRateParameter.Value = new DoubleValue(0.39763536438352531);
+      #endregion
+      return es;
+    }
+    #endregion
+    #endregion
+
+    #region Island GA
+    #region TSP
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateIslandGaTspSampleTest() {
+      var ga = CreateIslandGaTspSample();
+      XmlGenerator.Serialize(ga, @"Samples\IslandGA_TSP.hl");
+    }
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunIslandGaTspSampleTest() {
+      var ga = CreateIslandGaTspSample();
+      ga.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(ga);
+      Assert.AreEqual(9918, SamplesUtils.GetDoubleResult(ga, "BestQuality"));
+      Assert.AreEqual(10324.64, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"));
+      Assert.AreEqual(11823, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"));
+      Assert.AreEqual(495500, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
+    }
+
+    private IslandGeneticAlgorithm CreateIslandGaTspSample() {
+      IslandGeneticAlgorithm ga = new IslandGeneticAlgorithm();
+      #region Problem Configuration
+      var provider = new TSPLIBTSPInstanceProvider();
+      var instance = provider.GetDataDescriptors().Where(x => x.Name == "ch130").Single();
+      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
+      tspProblem.Load(provider.LoadData(instance));
+      tspProblem.UseDistanceMatrix.Value = true;
+      #endregion
+      #region Algorithm Configuration
+      ga.Name = "Island Genetic Algorithm - TSP";
+      ga.Description = "An island genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
+      ga.Problem = tspProblem;
+      SamplesUtils.ConfigureIslandGeneticAlgorithmParameters<ProportionalSelector, OrderCrossover2, InversionManipulator,
+        UnidirectionalRingMigrator, BestSelector, WorstReplacer>(
+        ga, 100, 1, 1000, 0.05, 5, 50, 0.25);
+      #endregion
+      return ga;
+    }
+    #endregion
+    #endregion
+
+    #region LS
+    #region Knapsack
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateLocalSearchKnapsackSampleTest() {
+      var ls = CreateLocalSearchKnapsackSample();
+      XmlGenerator.Serialize(ls, @"Samples\LS_Knapsack.hl");
+    }
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "medium")]
+    public void RunLocalSearchKnapsackSampleTest() {
+      var ls = CreateLocalSearchKnapsackSample();
+      ls.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(ls);
+      Assert.AreEqual(345, SamplesUtils.GetDoubleResult(ls, "BestQuality"));
+      Assert.AreEqual(340.70731707317071, SamplesUtils.GetDoubleResult(ls, "CurrentAverageQuality"));
+      Assert.AreEqual(337, SamplesUtils.GetDoubleResult(ls, "CurrentWorstQuality"));
+      Assert.AreEqual(82000, SamplesUtils.GetIntResult(ls, "EvaluatedMoves"));
+    }
+
+    private LocalSearch CreateLocalSearchKnapsackSample() {
+      LocalSearch ls = new LocalSearch();
+      #region Problem Configuration
+      KnapsackProblem problem = new KnapsackProblem();
+      problem.BestKnownQuality = new DoubleValue(362);
+      problem.BestKnownSolution = new HeuristicLab.Encodings.BinaryVectorEncoding.BinaryVector(new bool[] { 
+       true , false, false, true , true , true , true , true , false, true , true , true , true , true , true , false, true , false, true , true , false, true , true , false, true , false, true , true , true , false, true , true , false, true , true , false, true , false, true , true , true , true , true , true , true , true , true , true , true , true , true , false, true , false, false, true , true , false, true , true , true , true , true , true , true , true , false, true , false, true , true , true , true , false, true , true , true , true , true , true , true , true});
+      problem.EvaluatorParameter.Value = new KnapsackEvaluator();
+      problem.SolutionCreatorParameter.Value = new RandomBinaryVectorCreator();
+      problem.KnapsackCapacity.Value = 297;
+      problem.Maximization.Value = true;
+      problem.Penalty.Value = 1;
+      problem.Values = new IntArray(new int[] { 
+  6, 1, 1, 6, 7, 8, 7, 4, 2, 5, 2, 6, 7, 8, 7, 1, 7, 1, 9, 4, 2, 6, 5,  3, 5, 3, 3, 6, 5, 2, 4, 9, 4, 5, 7, 1, 4, 3, 5, 5, 8, 3, 6, 7, 3, 9, 7, 7, 5, 5, 7, 1, 4, 4, 3, 9, 5, 1, 6, 2, 2, 6, 1, 6, 5, 4, 4, 7, 1,  8, 9, 9, 7, 4, 3, 8, 7, 5, 7, 4, 4, 5});
+      problem.Weights = new IntArray(new int[] { 
+ 1, 9, 3, 6, 5, 3, 8, 1, 7, 4, 2, 1, 2, 7, 9, 9, 8, 4, 9, 2, 4, 8, 3, 7, 5, 7, 5, 5, 1, 9, 8, 7, 8, 9, 1, 3, 3, 8, 8, 5, 1, 2, 4, 3, 6, 9, 4, 4, 9, 7, 4, 5, 1, 9, 7, 6, 7, 4, 7, 1, 2, 1, 2, 9, 8, 6, 8, 4, 7, 6, 7, 5, 3, 9, 4, 7, 4, 6, 1, 2, 5, 4});
+      problem.Name = "Knapsack Problem";
+      problem.Description = "Represents a Knapsack problem.";
+      #endregion
+      #region Algorithm Configuration
+      ls.Name = "Local Search - Knapsack";
+      ls.Description = "A local search algorithm that solves a randomly generated Knapsack problem";
+      ls.Problem = problem;
+      ls.MaximumIterations.Value = 1000;
+      ls.MoveEvaluator = ls.MoveEvaluatorParameter.ValidValues
+        .OfType<KnapsackOneBitflipMoveEvaluator>()
+        .Single();
+      ls.MoveGenerator = ls.MoveGeneratorParameter.ValidValues
+        .OfType<ExhaustiveOneBitflipMoveGenerator>()
+        .Single();
+      ls.MoveMaker = ls.MoveMakerParameter.ValidValues
+        .OfType<OneBitflipMoveMaker>()
+        .Single();
+      ls.SampleSize.Value = 100;
+      ls.Seed.Value = 0;
+      ls.SetSeedRandomly.Value = true;
+      #endregion
+      ls.Engine = new ParallelEngine.ParallelEngine();
+      return ls;
+    }
+    #endregion
+    #endregion
+
+    #region PSO
+    #region Schwefel
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreatePsoSchwefelSampleTest() {
+      var pso = CreatePsoSchwefelSample();
+      XmlGenerator.Serialize(pso, @"Samples\PSO_Schwefel.hl");
+    }
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "medium")]
+    public void RunPsoSchwefelSampleTest() {
+      var pso = CreatePsoSchwefelSample();
+      pso.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(pso);
+      if (!Environment.Is64BitProcess) {
+        Assert.AreEqual(118.44027985932837, SamplesUtils.GetDoubleResult(pso, "BestQuality"));
+        Assert.AreEqual(140.71570105946438, SamplesUtils.GetDoubleResult(pso, "CurrentAverageQuality"));
+        Assert.AreEqual(220.956806502853, SamplesUtils.GetDoubleResult(pso, "CurrentWorstQuality"));
+        Assert.AreEqual(1000, SamplesUtils.GetIntResult(pso, "Iterations"));
+      } else {
+        Assert.AreEqual(118.43958282879345, SamplesUtils.GetDoubleResult(pso, "BestQuality"));
+        Assert.AreEqual(139.43946864779372, SamplesUtils.GetDoubleResult(pso, "CurrentAverageQuality"));
+        Assert.AreEqual(217.14654589055152, SamplesUtils.GetDoubleResult(pso, "CurrentWorstQuality"));
+        Assert.AreEqual(1000, SamplesUtils.GetIntResult(pso, "Iterations"));
+      }
+    }
+    private ParticleSwarmOptimization CreatePsoSchwefelSample() {
+      ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
+      #region Problem Configuration
+      var problem = new SingleObjectiveTestFunctionProblem();
+      problem.BestKnownQuality.Value = 0.0;
+      problem.BestKnownSolutionParameter.Value = new RealVector(new double[] { 420.968746, 420.968746 });
+      problem.Bounds = new DoubleMatrix(new double[,] { { -500, 500 } });
+      problem.EvaluatorParameter.Value = new SchwefelEvaluator();
+      problem.Maximization.Value = false;
+      problem.ProblemSize.Value = 2;
+      problem.SolutionCreatorParameter.Value = new UniformRandomRealVectorCreator();
+      #endregion
+      #region Algorithm Configuration
+      pso.Name = "Particle Swarm Optimization - Schwefel";
+      pso.Description = "A particle swarm optimization algorithm which solves the 2-dimensional Schwefel test function (based on the description in Pedersen, M.E.H. (2010). PhD thesis. University of Southampton)";
+      pso.Problem = problem;
+      pso.Inertia.Value = 10;
+      pso.MaxIterations.Value = 1000;
+      pso.NeighborBestAttraction.Value = 0.5;
+      pso.PersonalBestAttraction.Value = -0.01;
+      pso.SwarmSize.Value = 50;
+
+      var inertiaUpdater = pso.InertiaUpdaterParameter.ValidValues
+        .OfType<ExponentialDiscreteDoubleValueModifier>()
+        .Single();
+      inertiaUpdater.StartValueParameter.Value = new DoubleValue(10);
+      inertiaUpdater.EndValueParameter.Value = new DoubleValue(1);
+      pso.InertiaUpdater = inertiaUpdater;
+
+      pso.ParticleCreator = pso.ParticleCreatorParameter.ValidValues
+        .OfType<RealVectorParticleCreator>()
+        .Single();
+      var swarmUpdater = pso.SwarmUpdaterParameter.ValidValues
+        .OfType<RealVectorSwarmUpdater>()
+        .Single();
+      swarmUpdater.VelocityBoundsIndexParameter.ActualName = "Iterations";
+      swarmUpdater.VelocityBoundsParameter.Value = new DoubleMatrix(new double[,] { { -10, 10 } });
+      swarmUpdater.VelocityBoundsStartValueParameter.Value = new DoubleValue(10.0);
+      swarmUpdater.VelocityBoundsEndValueParameter.Value = new DoubleValue(1.0);
+      swarmUpdater.VelocityBoundsScalingOperatorParameter.Value = swarmUpdater.VelocityBoundsScalingOperatorParameter.ValidValues
+        .OfType<ExponentialDiscreteDoubleValueModifier>()
+        .Single();
+
+      pso.TopologyInitializer = null;
+      pso.TopologyUpdater = null;
+      pso.SwarmUpdater = swarmUpdater;
+      pso.Seed.Value = 0;
+      pso.SetSeedRandomly.Value = true;
+      #endregion
+      pso.Engine = new ParallelEngine.ParallelEngine();
+      return pso;
+    }
+    #endregion
+    #endregion
+
+    #region SA
+    #region Rastrigin
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateSimulatedAnnealingRastriginSampleTest() {
+      var sa = CreateSimulatedAnnealingRastriginSample();
+      XmlGenerator.Serialize(sa, @"Samples\SA_Rastrigin.hl");
+    }
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "medium")]
+    public void RunSimulatedAnnealingRastriginSampleTest() {
+      var sa = CreateSimulatedAnnealingRastriginSample();
+      sa.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(sa);
+      Assert.AreEqual(0.00014039606034543795, SamplesUtils.GetDoubleResult(sa, "BestQuality"));
+      Assert.AreEqual(5000, SamplesUtils.GetIntResult(sa, "EvaluatedMoves"));
+    }
+    private SimulatedAnnealing CreateSimulatedAnnealingRastriginSample() {
+      SimulatedAnnealing sa = new SimulatedAnnealing();
+      #region Problem Configuration
+      var problem = new SingleObjectiveTestFunctionProblem();
+      problem.BestKnownQuality.Value = 0.0;
+      problem.BestKnownSolutionParameter.Value = new RealVector(new double[] { 0, 0 });
+      problem.Bounds = new DoubleMatrix(new double[,] { { -5.12, 5.12 } });
+      problem.EvaluatorParameter.Value = new RastriginEvaluator();
+      problem.Maximization.Value = false;
+      problem.ProblemSize.Value = 2;
+      problem.SolutionCreatorParameter.Value = new UniformRandomRealVectorCreator();
+      #endregion
+      #region Algorithm Configuration
+      sa.Name = "Simulated Annealing - Rastrigin";
+      sa.Description = "A simulated annealing algorithm that solves the 2-dimensional Rastrigin test function";
+      sa.Problem = problem;
+      var annealingOperator = sa.AnnealingOperatorParameter.ValidValues
+        .OfType<ExponentialDiscreteDoubleValueModifier>()
+        .Single();
+      annealingOperator.StartIndexParameter.Value = new IntValue(0);
+      sa.AnnealingOperator = annealingOperator;
+
+      sa.EndTemperature.Value = 1E-6;
+      sa.InnerIterations.Value = 50;
+      sa.MaximumIterations.Value = 100;
+      var moveEvaluator = sa.MoveEvaluatorParameter.ValidValues
+        .OfType<RastriginAdditiveMoveEvaluator>()
+        .Single();
+      moveEvaluator.A.Value = 10;
+      sa.MoveEvaluator = moveEvaluator;
+
+      var moveGenerator = sa.MoveGeneratorParameter.ValidValues
+        .OfType<StochasticNormalMultiMoveGenerator>()
+        .Single();
+      moveGenerator.SigmaParameter.Value = new DoubleValue(1);
+      sa.MoveGenerator = moveGenerator;
+
+      sa.MoveMaker = sa.MoveMakerParameter.ValidValues
+        .OfType<AdditiveMoveMaker>()
+        .Single();
+
+      sa.Seed.Value = 0;
+      sa.SetSeedRandomly.Value = true;
+      sa.StartTemperature.Value = 1;
+      #endregion
+      sa.Engine = new ParallelEngine.ParallelEngine();
+      return sa;
+    }
+    #endregion
+    #endregion
+
+    #region TS
+    #region TSP
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateTabuSearchTspSampleTest() {
+      var ts = CreateTabuSearchTspSample();
+      XmlGenerator.Serialize(ts, @"Samples\TS_TSP.hl");
+    }
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunTabuSearchTspSampleTest() {
+      var ts = CreateTabuSearchTspSample();
+      ts.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(ts);
+      Assert.AreEqual(6294, SamplesUtils.GetDoubleResult(ts, "BestQuality"));
+      Assert.AreEqual(7380.0386666666664, SamplesUtils.GetDoubleResult(ts, "CurrentAverageQuality"));
+      Assert.AreEqual(8328, SamplesUtils.GetDoubleResult(ts, "CurrentWorstQuality"));
+      Assert.AreEqual(750000, SamplesUtils.GetIntResult(ts, "EvaluatedMoves"));
+    }
+
+    private TabuSearch CreateTabuSearchTspSample() {
+      TabuSearch ts = new TabuSearch();
+      #region Problem Configuration
+      var provider = new TSPLIBTSPInstanceProvider();
+      var instance = provider.GetDataDescriptors().Where(x => x.Name == "ch130").Single();
+      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
+      tspProblem.Load(provider.LoadData(instance));
+      tspProblem.UseDistanceMatrix.Value = true;
+      #endregion
+      #region Algorithm Configuration
+      ts.Name = "Tabu Search - TSP";
+      ts.Description = "A tabu search algorithm that solves the \"ch130\" TSP (imported from TSPLIB)";
+      ts.Problem = tspProblem;
+
+      ts.MaximumIterations.Value = 1000;
+      // move generator has to be set first
+      var moveGenerator = ts.MoveGeneratorParameter.ValidValues
+        .OfType<StochasticInversionMultiMoveGenerator>()
+        .Single();
+      ts.MoveGenerator = moveGenerator;
+      var moveEvaluator = ts.MoveEvaluatorParameter.ValidValues
+        .OfType<TSPInversionMoveRoundedEuclideanPathEvaluator>()
+        .Single();
+      ts.MoveEvaluator = moveEvaluator;
+      var moveMaker = ts.MoveMakerParameter.ValidValues
+        .OfType<InversionMoveMaker>()
+        .Single();
+      ts.MoveMaker = moveMaker;
+      ts.SampleSize.Value = 750;
+      ts.Seed.Value = 0;
+      ts.SetSeedRandomly.Value = true;
+
+      var tabuChecker = ts.TabuCheckerParameter.ValidValues
+        .OfType<InversionMoveSoftTabuCriterion>()
+        .Single();
+      tabuChecker.UseAspirationCriterion.Value = true;
+      ts.TabuChecker = tabuChecker;
+
+      var tabuMaker = ts.TabuMakerParameter.ValidValues
+        .OfType<InversionMoveTabuMaker>()
+        .Single();
+      ts.TabuMaker = tabuMaker;
+      ts.TabuTenure.Value = 60;
+
+      #endregion
+      ts.Engine = new ParallelEngine.ParallelEngine();
+      return ts;
+    }
+    #endregion
+
+    #region VRP
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateTabuSearchVRPSampleTest() {
+      var vrp = CreateTabuSearchVrpSample();
+      XmlGenerator.Serialize(vrp, @"Samples\TS_VRP.hl");
+    }
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunTabuSearchVRPSampleTest() {
+      var vrp = CreateTabuSearchVrpSample();
+      vrp.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(vrp);
+      Assert.AreEqual(1473, SamplesUtils.GetDoubleResult(vrp, "BestQuality"));
+      Assert.AreEqual(2102.1192622950812, SamplesUtils.GetDoubleResult(vrp, "CurrentAverageQuality"));
+      Assert.AreEqual(4006, SamplesUtils.GetDoubleResult(vrp, "CurrentWorstQuality"));
+      Assert.AreEqual(119072, SamplesUtils.GetIntResult(vrp, "EvaluatedMoves"));
+    }
+
+    private TabuSearch CreateTabuSearchVrpSample() {
+      TabuSearch ts = new TabuSearch();
+      #region Problem Configuration
+      var provider = new AugeratInstanceProvider();
+      var instance = provider.GetDataDescriptors().Where(x => x.Name == "A-n62-k8").Single();
+      VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
+      vrpProblem.Load(provider.LoadData(instance));
+      #endregion
+      #region Algorithm Configuration
+      ts.Name = "Tabu Search - VRP";
+      ts.Description = "A tabu search algorithm that solves the \"A-n62-k8\" VRP (imported from Augerat)";
+      ts.Problem = vrpProblem;
+
+      ts.MaximumIterations.Value = 200;
+      // move generator has to be set first
+      var moveGenerator = ts.MoveGeneratorParameter.ValidValues
+        .OfType<PotvinCustomerRelocationExhaustiveMoveGenerator>()
+        .Single();
+      ts.MoveGenerator = moveGenerator;
+      var moveEvaluator = ts.MoveEvaluatorParameter.ValidValues
+        .OfType<PotvinCustomerRelocationMoveEvaluator>()
+        .Single();
+      ts.MoveEvaluator = moveEvaluator;
+      var moveMaker = ts.MoveMakerParameter.ValidValues
+        .OfType<PotvinCustomerRelocationMoveMaker>()
+        .Single();
+      ts.MoveMaker = moveMaker;
+      ts.SampleSize.Value = 1000;
+      ts.Seed.Value = 0;
+      ts.SetSeedRandomly.Value = true;
+
+      var tabuChecker = ts.TabuCheckerParameter.ValidValues
+        .OfType<PotvinCustomerRelocationMoveTabuCriterion>()
+        .Single();
+      tabuChecker.UseAspirationCriterion.Value = false;
+      ts.TabuChecker = tabuChecker;
+
+      var tabuMaker = ts.TabuMakerParameter.ValidValues
+        .OfType<PotvinCustomerRelocationMoveTabuMaker>()
+        .Single();
+      ts.TabuMaker = tabuMaker;
+      ts.TabuTenure.Value = 6;
+
+      #endregion
+      ts.Engine = new ParallelEngine.ParallelEngine();
+      return ts;
+    }
+    #endregion
+    #endregion
+
+    #region VNS
+    #region TSP
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateVnsTspSampleTest() {
+      var vns = CreateVnsTspSample();
+      XmlGenerator.Serialize(vns, @"Samples\VNS_TSP.hl");
+    }
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunVnsTspSampleTest() {
+      var vns = CreateVnsTspSample();
+      vns.SetSeedRandomly = false;
+      SamplesUtils.RunAlgorithm(vns);
+      Assert.AreEqual(867, SamplesUtils.GetDoubleResult(vns, "BestQuality"));
+      Assert.AreEqual(867, SamplesUtils.GetDoubleResult(vns, "CurrentAverageQuality"));
+      Assert.AreEqual(867, SamplesUtils.GetDoubleResult(vns, "CurrentWorstQuality"));
+      Assert.AreEqual(12975173, SamplesUtils.GetIntResult(vns, "EvaluatedSolutions"));
+    }
+
+    private VariableNeighborhoodSearch CreateVnsTspSample() {
+      VariableNeighborhoodSearch vns = new VariableNeighborhoodSearch();
+      #region Problem Configuration
+      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
+      tspProblem.BestKnownSolution = new Permutation(PermutationTypes.Absolute, new int[] {
+117, 65, 73, 74, 75, 76, 82, 86, 87, 94, 100, 106, 115, 120, 124, 107, 101, 108, 109, 102, 97, 90, 96, 95, 88, 89, 84, 78, 69, 57, 68, 56, 44, 55, 45, 36, 46, 37, 38, 47, 48, 59, 49, 58, 70, 77, 83, 79, 50, 80, 85, 98, 103, 110, 116, 121, 125, 133, 132, 138, 139, 146, 147, 159, 168, 169, 175, 182, 188, 201, 213, 189, 214, 221, 230, 246, 262, 276, 284, 275, 274, 261, 245, 229, 220, 228, 243, 259, 273, 282, 272, 258, 242, 257, 293, 292, 302, 310, 319, 320, 327, 326, 333, 340, 346, 339, 345, 344, 337, 338, 332, 325, 318, 309, 301, 291, 271, 251, 270, 233, 250, 269, 268, 280, 290, 300, 415, 440, 416, 417, 441, 458, 479, 418, 419, 395, 420, 442, 421, 396, 397, 422, 423, 461, 481, 502, 460, 501, 459, 480, 500, 517, 531, 516, 530, 499, 478, 457, 439, 414, 413, 412, 438, 456, 477, 498, 515, 529, 538, 547, 558, 559, 560, 548, 539, 549, 561, 562, 551, 550, 532, 540, 533, 541, 518, 534, 542, 552, 553, 554, 555, 535, 543, 556, 544, 536, 522, 505, 521, 520, 504, 519, 503, 482, 462, 463, 464, 483, 443, 465, 484, 506, 485, 507, 508, 487, 467, 486, 466, 445, 428, 444, 424, 425, 426, 427, 398, 399, 400, 381, 382, 371, 372, 401, 429, 446, 430, 402, 383, 366, 356, 357, 352, 385, 384, 403, 431, 447, 469, 468, 488, 489, 490, 470, 471, 448, 432, 433, 404, 405, 386, 373, 374, 367, 376, 375, 387, 491, 509, 537, 510, 492, 472, 449, 388, 389, 406, 450, 407, 377, 368, 359, 354, 350, 335, 324, 330, 390, 434, 451, 473, 493, 511, 523, 545, 563, 565, 567, 570, 569, 578, 577, 576, 575, 574, 573, 572, 580, 584, 583, 582, 587, 586, 585, 581, 579, 571, 568, 566, 564, 557, 546, 527, 513, 526, 525, 524, 512, 495, 494, 474, 452, 436, 409, 435, 453, 475, 496, 514, 528, 497, 455, 476, 454, 437, 411, 410, 394, 393, 392, 380, 370, 379, 408, 391, 378, 369, 364, 365, 361, 355, 351, 343, 336, 331, 317, 299, 286, 287, 278, 263, 264, 265, 223, 202, 248, 266, 279, 288, 289, 281, 267, 249, 232, 224, 216, 215, 204, 192, 193, 194, 186, 179, 185, 203, 191, 190, 177, 171, 161, 128, 135, 140, 149, 162, 150, 163, 172, 178, 173, 164, 152, 151, 141, 153, 165, 154, 142, 155, 143, 137, 136, 130, 129, 118, 114, 113, 105, 119, 123, 131, 144, 156, 157, 145, 158, 166, 167, 174, 180, 181, 187, 195, 205, 217, 226, 236, 225, 234, 252, 235, 253, 254, 255, 238, 239, 240, 241, 256, 237, 206, 207, 208, 196, 197, 198, 209, 199, 200, 211, 212, 219, 210, 218, 227, 244, 260, 283, 294, 295, 303, 296, 311, 304, 297, 298, 305, 285, 306, 314, 329, 321, 313, 312, 328, 334, 341, 347, 348, 353, 358, 362, 363, 360, 349, 342, 322, 323, 315, 316, 308, 307, 277, 247, 231, 222, 184, 183, 176, 170, 160, 148, 134, 127, 126, 111, 104, 92, 91, 71, 60, 51, 52, 40, 32, 23, 21, 20, 18, 17, 16, 14, 13, 11, 10, 7, 6, 5, 2, 1, 0, 3, 4, 31, 39, 25, 30, 35, 34, 33, 43, 54, 42, 27, 28, 29, 9, 8, 12, 15, 19, 22, 24, 26, 41, 67, 66, 64, 63, 53, 62, 61, 72, 81, 93, 99, 112, 122, 
+      });
+      tspProblem.Coordinates = new DoubleMatrix(new double[,] {
+{48, 71}, {49, 71}, {50, 71}, {44, 70}, {45, 70}, {52, 70}, {53, 70}, {54, 70}, {41, 69}, {42, 69}, {55, 69}, {56, 69}, {40, 68}, {56, 68}, {57, 68}, {39, 67}, {57, 67}, {58, 67}, {59, 67}, {38, 66}, {59, 66}, {60, 66}, {37, 65}, {60, 65}, {36, 64}, {43, 64}, {35, 63}, {37, 63}, {41, 63}, {42, 63}, {43, 63}, {47, 63}, {61, 63}, {40, 62}, {41, 62}, {42, 62}, {43, 62}, {45, 62}, {46, 62}, {47, 62}, {62, 62}, {34, 61}, {38, 61}, {39, 61}, {42, 61}, {43, 61}, {44, 61}, {45, 61}, {46, 61}, {47, 61}, {52, 61}, {62, 61}, {63, 61}, {26, 60}, {38, 60}, {42, 60}, {43, 60}, {44, 60}, {46, 60}, {47, 60}, {63, 60}, {23, 59}, {24, 59}, {27, 59}, {29, 59}, {30, 59}, {31, 59}, {33, 59}, {42, 59}, {46, 59}, {47, 59}, {63, 59}, {21, 58}, {32, 58}, {33, 58}, {34, 58}, {35, 58}, {46, 58}, {47, 58}, {48, 58}, {53, 58}, {21, 57}, {35, 57}, {47, 57}, {48, 57}, {53, 57}, {36, 56}, {37, 56}, {46, 56}, {47, 56}, {48, 56}, {64, 56}, {65, 56}, {20, 55}, {38, 55}, {46, 55}, {47, 55}, {48, 55}, {52, 55}, {21, 54}, {40, 54}, {47, 54}, {48, 54}, {52, 54}, {65, 54}, {30, 53}, {41, 53}, {46, 53}, {47, 53}, {48, 53}, {52, 53}, {65, 53}, {21, 52}, {32, 52}, {33, 52}, {42, 52}, {51, 52}, {21, 51}, {33, 51}, {34, 51}, {43, 51}, {51, 51}, {21, 50}, {35, 50}, {44, 50}, {50, 50}, {66, 50}, {67, 50}, {21, 49}, {34, 49}, {36, 49}, {37, 49}, {46, 49}, {49, 49}, {67, 49}, {22, 48}, {36, 48}, {37, 48}, {46, 48}, {47, 48}, {22, 47}, {30, 47}, {34, 47}, {37, 47}, {38, 47}, {39, 47}, {47, 47}, {48, 47}, {67, 47}, {23, 46}, {28, 46}, {29, 46}, {30, 46}, {31, 46}, {32, 46}, {35, 46}, {37, 46}, {38, 46}, {39, 46}, {49, 46}, {67, 46}, {23, 45}, {28, 45}, {29, 45}, {31, 45}, {32, 45}, {40, 45}, {41, 45}, {49, 45}, {50, 45}, {68, 45}, {24, 44}, {29, 44}, {32, 44}, {41, 44}, {51, 44}, {68, 44}, {25, 43}, {30, 43}, {32, 43}, {42, 43}, {43, 43}, {51, 43}, {68, 43}, {69, 43}, {31, 42}, {32, 42}, {43, 42}, {52, 42}, {55, 42}, {26, 41}, {27, 41}, {31, 41}, {32, 41}, {33, 41}, {44, 41}, {45, 41}, {46, 41}, {47, 41}, {48, 41}, {49, 41}, {53, 41}, {25, 40}, {27, 40}, {32, 40}, {43, 40}, {44, 40}, {45, 40}, {46, 40}, {48, 40}, {49, 40}, {50, 40}, {51, 40}, {53, 40}, {56, 40}, {32, 39}, {33, 39}, {43, 39}, {50, 39}, {51, 39}, {54, 39}, {56, 39}, {69, 39}, {24, 38}, {32, 38}, {41, 38}, {42, 38}, {51, 38}, {52, 38}, {54, 38}, {57, 38}, {69, 38}, {31, 37}, {32, 37}, {40, 37}, {41, 37}, {42, 37}, {43, 37}, {44, 37}, {45, 37}, {46, 37}, {47, 37}, {48, 37}, {51, 37}, {52, 37}, {55, 37}, {57, 37}, {69, 37}, {24, 36}, {31, 36}, {32, 36}, {39, 36}, {40, 36}, {41, 36}, {42, 36}, {43, 36}, {45, 36}, {48, 36}, {49, 36}, {51, 36}, {53, 36}, {55, 36}, {58, 36}, {22, 35}, {23, 35}, {24, 35}, {25, 35}, {30, 35}, {31, 35}, {32, 35}, {39, 35}, {41, 35}, {49, 35}, {51, 35}, {55, 35}, {56, 35}, {58, 35}, {71, 35}, {20, 34}, {27, 34}, {30, 34}, {31, 34}, {51, 34}, {53, 34}, {57, 34}, {60, 34}, {18, 33}, {19, 33}, {29, 33}, {30, 33}, {31, 33}, {45, 33}, {46, 33}, {47, 33}, {52, 33}, {53, 33}, {55, 33}, {57, 33}, {58, 33}, {17, 32}, {30, 32}, {44, 32}, {47, 32}, {54, 32}, {57, 32}, {59, 32}, {61, 32}, {71, 32}, {72, 32}, {43, 31}, {47, 31}, {56, 31}, {58, 31}, {59, 31}, {61, 31}, {72, 31}, {74, 31}, {16, 30}, {43, 30}, {46, 30}, {47, 30}, {59, 30}, {63, 30}, {71, 30}, {75, 30}, {43, 29}, {46, 29}, {47, 29}, {59, 29}, {60, 29}, {75, 29}, {15, 28}, {43, 28}, {46, 28}, {61, 28}, {76, 28}, {15, 27}, {43, 27}, {44, 27}, {45, 27}, {46, 27}, {60, 27}, {62, 27}, {15, 26}, {43, 26}, {44, 26}, {46, 26}, {59, 26}, {60, 26}, {64, 26}, {77, 26}, {15, 25}, {58, 25}, {61, 25}, {77, 25}, {15, 24}, {53, 24}, {55, 24}, {61, 24}, {77, 24}, {62, 23}, {16, 22}, {61, 22}, {62, 22}, {15, 21}, {16, 21}, {52, 21}, {63, 21}, {77, 21}, {16, 20}, {17, 20}, {46, 20}, {47, 20}, {60, 20}, {62, 20}, {63, 20}, {65, 20}, {76, 20}, {15, 19}, {17, 19}, {18, 19}, {44, 19}, {45, 19}, {48, 19}, {53, 19}, {56, 19}, {60, 19}, {62, 19}, {67, 19}, {68, 19}, {76, 19}, {15, 18}, {18, 18}, {19, 18}, {20, 18}, {32, 18}, {33, 18}, {34, 18}, {41, 18}, {42, 18}, {43, 18}, {46, 18}, {48, 18}, {53, 18}, {59, 18}, {60, 18}, {69, 18}, {75, 18}, {16, 17}, {17, 17}, {20, 17}, {21, 17}, {22, 17}, {23, 17}, {24, 17}, {26, 17}, {28, 17}, {29, 17}, {30, 17}, {31, 17}, {32, 17}, {34, 17}, {35, 17}, {36, 17}, {37, 17}, {38, 17}, {39, 17}, {40, 17}, {44, 17}, {46, 17}, {48, 17}, {53, 17}, {56, 17}, {58, 17}, {75, 17}, {17, 16}, {18, 16}, {20, 16}, {24, 16}, {26, 16}, {27, 16}, {29, 16}, {33, 16}, {41, 16}, {42, 16}, {44, 16}, {47, 16}, {52, 16}, {57, 16}, {70, 16}, {73, 16}, {74, 16}, {17, 15}, {18, 15}, {20, 15}, {22, 15}, {24, 15}, {27, 15}, {29, 15}, {31, 15}, {33, 15}, {35, 15}, {36, 15}, {38, 15}, {39, 15}, {42, 15}, {45, 15}, {47, 15}, {52, 15}, {53, 15}, {55, 15}, {56, 15}, {70, 15}, {73, 15}, {17, 14}, {19, 14}, {21, 14}, {24, 14}, {26, 14}, {29, 14}, {31, 14}, {34, 14}, {37, 14}, {40, 14}, {42, 14}, {44, 14}, {46, 14}, {47, 14}, {53, 14}, {54, 14}, {55, 14}, {62, 14}, {70, 14}, {72, 14}, {17, 13}, {19, 13}, {21, 13}, {23, 13}, {25, 13}, {27, 13}, {30, 13}, {32, 13}, {34, 13}, {36, 13}, {38, 13}, {41, 13}, {43, 13}, {44, 13}, {45, 13}, {60, 13}, {70, 13}, {71, 13}, {18, 12}, {21, 12}, {23, 12}, {26, 12}, {28, 12}, {31, 12}, {34, 12}, {37, 12}, {39, 12}, {41, 12}, {42, 12}, {70, 12}, {18, 11}, {19, 11}, {20, 11}, {21, 11}, {24, 11}, {25, 11}, {27, 11}, {29, 11}, {31, 11}, {33, 11}, {35, 11}, {38, 11}, {41, 11}, {59, 11}, {26, 10}, {29, 10}, {32, 10}, {34, 10}, {36, 10}, {39, 10}, {40, 10}, {69, 10}, {21, 9}, {26, 9}, {28, 9}, {30, 9}, {32, 9}, {33, 9}, {35, 9}, {36, 9}, {37, 9}, {38, 9}, {39, 9}, {22, 8}, {27, 8}, {28, 8}, {29, 8}, {30, 8}, {31, 8}, {68, 8}, {23, 7}, {66, 7}, {24, 6}, {65, 6}, {25, 5}, {62, 5}, {63, 5}, {26, 4}, {55, 4}, {56, 4}, {57, 4}, {58, 4}, {59, 4}, {60, 4}, {61, 4}, {28, 3}, {53, 3}, {29, 2}, {50, 2}, {51, 2}, {52, 2}, {31, 1}, {32, 1}, {48, 1}
+      });
+      tspProblem.BestKnownQuality = new DoubleValue(867);
+
+      tspProblem.EvaluatorParameter.Value = new TSPRoundedEuclideanPathEvaluator();
+      tspProblem.SolutionCreatorParameter.Value = new RandomPermutationCreator();
+      tspProblem.UseDistanceMatrix.Value = true;
+      tspProblem.Name = "Funny TSP";
+      tspProblem.Description = "Represents a symmetric Traveling Salesman Problem.";
+      #endregion
+      #region Algorithm Configuration
+      vns.Name = "Variable Neighborhood Search - TSP";
+      vns.Description = "A variable neighborhood search algorithm which solves a funny TSP instance";
+      vns.Problem = tspProblem;
+
+      var localImprovement = vns.LocalImprovementParameter.ValidValues
+        .OfType<LocalSearchImprovementOperator>()
+        .Single();
+      // move generator has to be set first
+      localImprovement.MoveGenerator = localImprovement.MoveGeneratorParameter.ValidValues
+        .OfType<StochasticInversionMultiMoveGenerator>()
+        .Single();
+      localImprovement.MoveEvaluator = localImprovement.MoveEvaluatorParameter.ValidValues
+        .OfType<TSPInversionMoveRoundedEuclideanPathEvaluator>()
+        .Single();
+      localImprovement.MoveMaker = localImprovement.MoveMakerParameter.ValidValues
+        .OfType<InversionMoveMaker>()
+        .Single();
+      localImprovement.SampleSizeParameter.Value = new IntValue(500);
+      vns.LocalImprovement = localImprovement;
+
+      vns.LocalImprovementMaximumIterations = 150;
+      vns.MaximumIterations = 25;
+      vns.Seed = 0;
+      vns.SetSeedRandomly = true;
+      var shakingOperator = vns.ShakingOperatorParameter.ValidValues
+        .OfType<PermutationShakingOperator>()
+        .Single();
+      shakingOperator.Operators.SetItemCheckedState(shakingOperator.Operators
+        .OfType<Swap2Manipulator>()
+        .Single(), false);
+      shakingOperator.Operators.SetItemCheckedState(shakingOperator.Operators
+        .OfType<Swap3Manipulator>()
+        .Single(), false);
+      vns.ShakingOperator = shakingOperator;
+      #endregion
+      vns.Engine = new ParallelEngine.ParallelEngine();
+      return vns;
+    }
+    #endregion
+    #endregion
+
+    #region Gaussian Process Regression
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateGaussianProcessRegressionSampleTest() {
+      var gpr = CreateGaussianProcessRegressionSample();
+      XmlGenerator.Serialize(gpr, @"Samples\GPR.hl");
+    }
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunGaussianProcessRegressionSample() {
+      var gpr = CreateGaussianProcessRegressionSample();
+      gpr.SetSeedRandomly = false;
+      gpr.Seed = 1618551877;
+      SamplesUtils.RunAlgorithm(gpr);
+      Assert.AreEqual(-940.48768748097029, SamplesUtils.GetDoubleResult(gpr, "NegativeLogLikelihood"));
+      Assert.AreEqual(0.99561947047986976, SamplesUtils.GetDoubleResult(gpr, "Training R²"));
+    }
+
+    private GaussianProcessRegression CreateGaussianProcessRegressionSample() {
+      var gpr = new GaussianProcessRegression();
+      var provider = new VariousInstanceProvider();
+      var instance = provider.GetDataDescriptors().Where(x => x.Name.Contains("Spatial co-evolution")).Single();
+      var regProblem = new RegressionProblem();
+      regProblem.Load(provider.LoadData(instance));
+      #region Algorithm Configuration
+      gpr.Name = "Gaussian Process Regression";
+      gpr.Description = "A Gaussian process regression algorithm which solves the spatial co-evolution benchmark problem";
+      gpr.Problem = regProblem;
+
+      gpr.CovarianceFunction = new CovarianceSquaredExponentialIso();
+      gpr.MeanFunction = new MeanConst();
+      gpr.MinimizationIterations = 20;
+      gpr.Seed = 0;
+      gpr.SetSeedRandomly = true;
+      #endregion
+      gpr.Engine = new ParallelEngine.ParallelEngine();
+      return gpr;
+    }
+    #endregion
+
+    #region Scatter Search
+    #region VRP
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateScatterSearchVRPSampleTest() {
+      var ss = CreateScatterSearchVRPSample();
+      XmlGenerator.Serialize(ss, @"Samples\SS_VRP.hl");
+    }
+
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunScatterSearchVRPSampleTest() {
+      var ss = CreateScatterSearchVRPSample();
+      ss.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(ss);
+      Assert.AreEqual(828.93686694283383, SamplesUtils.GetDoubleResult(ss, "BestQuality"));
+      Assert.AreEqual(868.63623986983077, SamplesUtils.GetDoubleResult(ss, "CurrentAverageQuality"));
+      Assert.AreEqual(1048.8333559209832, SamplesUtils.GetDoubleResult(ss, "CurrentWorstQuality"));
+      Assert.AreEqual(262622, SamplesUtils.GetIntResult(ss, "EvaluatedSolutions"));
+    }
+
+    private ScatterSearch CreateScatterSearchVRPSample() {
+      #region Problem Configuration
+      var provider = new SolomonInstanceProvider();
+      var instance = provider.GetDataDescriptors().Single(x => x.Name == "C101");
+      VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
+      vrpProblem.Load(provider.LoadData(instance));
+      #endregion
+
+      #region Algorithm Configuration
+      ScatterSearch ss = new ScatterSearch();
+      ss.Engine = new SequentialEngine.SequentialEngine();
+      ss.Name = "Scatter Search - VRP";
+      ss.Description = "A scatter search algorithm which solves the \"C101\" vehicle routing problem (imported from Solomon)";
+      ss.Problem = vrpProblem;
+
+      var improver = ss.Problem.Operators.OfType<VRPIntraRouteImprovementOperator>().First();
+      improver.ImprovementAttemptsParameter.Value.Value = 15;
+      improver.SampleSizeParameter.Value.Value = 10;
+      ss.Improver = improver;
+
+      var pathRelinker = ss.Problem.Operators.OfType<VRPPathRelinker>().First();
+      pathRelinker.IterationsParameter.Value.Value = 25;
+      ss.PathRelinker = pathRelinker;
+
+      var similarityCalculator = ss.SimilarityCalculatorParameter.ValidValues.OfType<VRPSimilarityCalculator>().First();
+      ss.SimilarityCalculator = similarityCalculator;
+
+      ss.MaximumIterations.Value = 2;
+      ss.PopulationSize.Value = 20;
+      ss.ReferenceSetSize.Value = 10;
+      ss.Seed.Value = 0;
+      return ss;
+      #endregion
+    }
+    #endregion
+    #endregion
+
+    #region RAPGA
+    #region Scheduling
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateRAPGASchedulingSampleTest() {
+      var ss = CreateRAPGASchedulingSample();
+      XmlGenerator.Serialize(ss, @"Samples\RAPGA_JSSP.hl");
+    }
+
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunRAPGASchedulingSampleTest() {
+      var rapga = CreateRAPGASchedulingSample();
+      rapga.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(rapga);
+      Assert.AreEqual(988.00, SamplesUtils.GetDoubleResult(rapga, "BestQuality"));
+      Assert.AreEqual(988.00, SamplesUtils.GetDoubleResult(rapga, "CurrentAverageQuality"));
+      Assert.AreEqual(988.00, SamplesUtils.GetDoubleResult(rapga, "CurrentWorstQuality"));
+      Assert.AreEqual(27100, SamplesUtils.GetIntResult(rapga, "EvaluatedSolutions"));
+    }
+
+    private RAPGA CreateRAPGASchedulingSample() {
+      #region Problem Configuration
+      JobShopSchedulingProblem problem = new JobShopSchedulingProblem();
+      #endregion
+
+      #region Algorithm Configuration
+      RAPGA rapga = new RAPGA();
+      rapga.Engine = new SequentialEngine.SequentialEngine();
+      rapga.Name = "RAPGA - Job Shop Scheduling";
+      rapga.Description = "A relevant alleles preserving genetic algorithm which solves a job shop scheduling problem";
+      rapga.Problem = problem;
+      rapga.Mutator = rapga.MutatorParameter.ValidValues.OfType<JSMSwapManipulator>().First();
+      rapga.Seed.Value = 0;
+      return rapga;
+      #endregion
+    }
+    #endregion
+    #endregion
+
+    #region grammatical evolution
+    #region artificial ant
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateGeArtificialAntSampleTest() {
+      var geaa = CreateGeArtificialAntSample();
+      XmlGenerator.Serialize(geaa, @"Samples\GE_ArtificialAnt.hl");
+    }
+
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunGeArtificalAntSampleTest() {
+      var ga = CreateGeArtificialAntSample();
+      ga.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(ga);
+    }
+
+    public OffspringSelectionGeneticAlgorithm CreateGeArtificialAntSample() {
+      OffspringSelectionGeneticAlgorithm ga = new OffspringSelectionGeneticAlgorithm();
+      #region Problem Configuration
+      var problem = new HeuristicLab.Problems.GrammaticalEvolution.GEArtificialAntProblem();
+      #endregion
+      #region Algorithm Configuration
+      ga.Name = "Grammatical Evolution - Artificial Ant (SantaFe)";
+      ga.Description = "Grammatical evolution algorithm for solving a artificial ant problem";
+      ga.Problem = problem;
+      SamplesUtils.ConfigureOsGeneticAlgorithmParameters<GenderSpecificSelector, Encodings.IntegerVectorEncoding.SinglePointCrossover, Encodings.IntegerVectorEncoding.UniformOnePositionManipulator>(
+        ga, 200, 1, 50, 0.05, 200);
+      #endregion
+      return ga;
+    }
+    #endregion
+
+    #region symbolic regression
+    #endregion
+    [TestMethod]
+    [TestCategory("Samples.Create")]
+    [TestProperty("Time", "medium")]
+    public void CreateGeSymbolicRegressionSampleTest() {
+      var geSymbReg = CreateGeSymbolicRegressionSample();
+      XmlGenerator.Serialize(geSymbReg, @"Samples\GE_SymbReg.hl");
+    }
+
+    [TestMethod]
+    [TestCategory("Samples.Execute")]
+    [TestProperty("Time", "long")]
+    public void RunGeSymbolicRegressionSampleTest() {
+      var ga = CreateGeSymbolicRegressionSample();
+      ga.SetSeedRandomly.Value = false;
+      SamplesUtils.RunAlgorithm(ga);
+    }
+
+    public OffspringSelectionGeneticAlgorithm CreateGeSymbolicRegressionSample() {
+      var ga = new OffspringSelectionGeneticAlgorithm();
+      #region Problem Configuration
+      var problem = new HeuristicLab.Problems.GrammaticalEvolution.GESymbolicRegressionSingleObjectiveProblem();
+
+      #endregion
+      #region Algorithm Configuration
+      ga.Name = "Grammatical Evolution - Symbolic Regression (Poly-10)";
+      ga.Description = "Grammatical evolution algorithm for solving a symbolic regression problem problem";
+      ga.Problem = problem;
+      problem.Load(new PolyTen().GenerateRegressionData());
+
+      // must occur after loading problem data because the grammar creates symbols for random constants once the data is loaded
+      var consts = problem.SymbolicExpressionTreeGrammar.AllowedSymbols.OfType<Constant>().ToList();
+      foreach (var c in consts) {
+        problem.SymbolicExpressionTreeGrammar.RemoveSymbol(c);
+      }
+
+      SamplesUtils.ConfigureOsGeneticAlgorithmParameters<GenderSpecificSelector, Encodings.IntegerVectorEncoding.SinglePointCrossover, Encodings.IntegerVectorEncoding.UniformOnePositionManipulator>(
+        ga, 1000, 1, 50, 0.05, 200);
+      #endregion
+      return ga;
+    }
+    #endregion
+
+    #region Helpers
+
+    #endregion
+  }
+}
Index: trunk/sources/HeuristicLab.Tests/HeuristicLab-3.3/Samples/SamplesUtils.cs
===================================================================
--- trunk/sources/HeuristicLab.Tests/HeuristicLab-3.3/Samples/SamplesUtils.cs	(revision 11051)
+++ trunk/sources/HeuristicLab.Tests/HeuristicLab-3.3/Samples/SamplesUtils.cs	(revision 11051)
@@ -0,0 +1,172 @@
+using System;
+using System.Linq;
+using System.Threading;
+using HeuristicLab.Algorithms.EvolutionStrategy;
+using HeuristicLab.Algorithms.GeneticAlgorithm;
+using HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm;
+using HeuristicLab.Data;
+using HeuristicLab.Optimization;
+using HeuristicLab.Selection;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+
+namespace HeuristicLab.Tests {
+  public static class SamplesUtils {
+    public const string Directory = @"Samples\";
+
+    public static void RunAlgorithm(IAlgorithm a) {
+      var trigger = new EventWaitHandle(false, EventResetMode.ManualReset);
+      Exception ex = null;
+      a.Stopped += (src, e) => { trigger.Set(); };
+      a.ExceptionOccurred += (src, e) => { ex = e.Value; trigger.Set(); };
+      a.Prepare();
+      a.Start();
+      trigger.WaitOne();
+
+      Assert.AreEqual(ex, null);
+    }
+
+    public static double GetDoubleResult(IAlgorithm a, string resultName) {
+      return ((DoubleValue)a.Results[resultName].Value).Value;
+    }
+
+    public static int GetIntResult(IAlgorithm a, string resultName) {
+      return ((IntValue)a.Results[resultName].Value).Value;
+    }
+
+    public static void ConfigureEvolutionStrategyParameters<R, M, SC, SR, SM>(EvolutionStrategy es, int popSize, int children, int parentsPerChild, int maxGens, bool plusSelection)
+      where R : ICrossover
+      where M : IManipulator
+      where SC : IStrategyParameterCreator
+      where SR : IStrategyParameterCrossover
+      where SM : IStrategyParameterManipulator {
+      es.PopulationSize.Value = popSize;
+      es.Children.Value = children;
+      es.ParentsPerChild.Value = parentsPerChild;
+      es.MaximumGenerations.Value = maxGens;
+      es.PlusSelection.Value = false;
+
+      es.Seed.Value = 0;
+      es.SetSeedRandomly.Value = true;
+
+      es.Recombinator = es.RecombinatorParameter.ValidValues
+        .OfType<R>()
+        .Single();
+
+      es.Mutator = es.MutatorParameter.ValidValues
+        .OfType<M>()
+        .Single();
+
+      es.StrategyParameterCreator = es.StrategyParameterCreatorParameter.ValidValues
+        .OfType<SC>()
+        .Single();
+      es.StrategyParameterCrossover = es.StrategyParameterCrossoverParameter.ValidValues
+        .OfType<SR>()
+        .Single();
+      es.StrategyParameterManipulator = es.StrategyParameterManipulatorParameter.ValidValues
+        .OfType<SM>()
+        .Single();
+      es.Engine = new ParallelEngine.ParallelEngine();
+    }
+
+    public static void ConfigureGeneticAlgorithmParameters<S, C, M>(GeneticAlgorithm ga, int popSize, int elites, int maxGens, double mutationRate, int tournGroupSize = 0)
+      where S : ISelector
+      where C : ICrossover
+      where M : IManipulator {
+      ga.Elites.Value = elites;
+      ga.MaximumGenerations.Value = maxGens;
+      ga.MutationProbability.Value = mutationRate;
+      ga.PopulationSize.Value = popSize;
+      ga.Seed.Value = 0;
+      ga.SetSeedRandomly.Value = true;
+      ga.Selector = ga.SelectorParameter.ValidValues
+        .OfType<S>()
+        .First();
+
+      ga.Crossover = ga.CrossoverParameter.ValidValues
+        .OfType<C>()
+        .First();
+
+      ga.Mutator = ga.MutatorParameter.ValidValues
+        .OfType<M>()
+        .First();
+
+      var tSelector = ga.Selector as TournamentSelector;
+      if (tSelector != null) {
+        tSelector.GroupSizeParameter.Value.Value = tournGroupSize;
+      }
+      ga.Engine = new ParallelEngine.ParallelEngine();
+    }
+
+    public static void ConfigureOsGeneticAlgorithmParameters<S, C, M>(OffspringSelectionGeneticAlgorithm ga, int popSize, int elites, int maxGens, double mutationRate = 0.05, double maxSelPres = 100, int tournGroupSize = 0)
+      where S : ISelector
+      where C : ICrossover
+      where M : IManipulator {
+      ga.Elites.Value = elites;
+      ga.MaximumGenerations.Value = maxGens;
+      ga.MutationProbability.Value = mutationRate;
+      ga.PopulationSize.Value = popSize;
+      ga.MaximumSelectionPressure.Value = maxSelPres;
+      ga.Seed.Value = 0;
+      ga.SetSeedRandomly.Value = true;
+      ga.ComparisonFactorLowerBound.Value = 1;
+      ga.ComparisonFactorUpperBound.Value = 1;
+
+      ga.Selector = ga.SelectorParameter.ValidValues
+        .OfType<S>()
+        .First();
+
+      ga.Crossover = ga.CrossoverParameter.ValidValues
+        .OfType<C>()
+        .First();
+
+      ga.Mutator = ga.MutatorParameter.ValidValues
+        .OfType<M>()
+        .First();
+
+      var tSelector = ga.Selector as TournamentSelector;
+      if (tSelector != null) {
+        tSelector.GroupSizeParameter.Value.Value = tournGroupSize;
+      }
+      ga.Engine = new ParallelEngine.ParallelEngine();
+    }
+
+    public static void ConfigureIslandGeneticAlgorithmParameters<S, C, M, Mi, MiS, MiR>(IslandGeneticAlgorithm ga, int popSize, int elites, int maxGens, double mutationRate, int numberOfIslands, int migrationInterval, double migrationRate)
+      where S : ISelector
+      where C : ICrossover
+      where M : IManipulator
+      where Mi : IMigrator
+      where MiS : ISelector
+      where MiR : IReplacer {
+      ga.Elites.Value = elites;
+      ga.MaximumGenerations.Value = maxGens;
+      ga.MutationProbability.Value = mutationRate;
+      ga.PopulationSize.Value = popSize;
+      ga.NumberOfIslands.Value = numberOfIslands;
+      ga.MigrationInterval.Value = migrationInterval;
+      ga.MigrationRate.Value = migrationRate;
+      ga.Seed.Value = 0;
+      ga.SetSeedRandomly.Value = true;
+      ga.Selector = ga.SelectorParameter.ValidValues
+        .OfType<S>()
+        .Single();
+
+      ga.Crossover = ga.CrossoverParameter.ValidValues
+        .OfType<C>()
+        .Single();
+
+      ga.Mutator = ga.MutatorParameter.ValidValues
+        .OfType<M>()
+        .Single();
+      ga.Migrator = ga.MigratorParameter.ValidValues
+        .OfType<Mi>()
+        .Single();
+      ga.EmigrantsSelector = ga.EmigrantsSelectorParameter.ValidValues
+        .OfType<MiS>()
+        .Single();
+      ga.ImmigrationReplacer = ga.ImmigrationReplacerParameter.ValidValues
+        .OfType<MiR>()
+        .Single();
+      ga.Engine = new ParallelEngine.ParallelEngine();
+    }
+  }
+}
Index: trunk/sources/HeuristicLab.Tests/HeuristicLab-3.3/SamplesTest.cs
===================================================================
--- trunk/sources/HeuristicLab.Tests/HeuristicLab-3.3/SamplesTest.cs	(revision 11050)
+++ 	(revision )
@@ -1,1438 +1,0 @@
-﻿#region License Information
-/* HeuristicLab
- * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
- *
- * This file is part of HeuristicLab.
- *
- * HeuristicLab is free software: you can redistribute it and/or modify
- * it under the terms of the GNU General Public License as published by
- * the Free Software Foundation, either version 3 of the License, or
- * (at your option) any later version.
- *
- * HeuristicLab is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- * GNU General Public License for more details.
- *
- * You should have received a copy of the GNU General Public License
- * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
- */
-#endregion
-
-using System;
-using System.IO;
-using System.Linq;
-using System.Threading;
-using HeuristicLab.Algorithms.DataAnalysis;
-using HeuristicLab.Algorithms.EvolutionStrategy;
-using HeuristicLab.Algorithms.GeneticAlgorithm;
-using HeuristicLab.Algorithms.LocalSearch;
-using HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm;
-using HeuristicLab.Algorithms.ParticleSwarmOptimization;
-using HeuristicLab.Algorithms.RAPGA;
-using HeuristicLab.Algorithms.ScatterSearch;
-using HeuristicLab.Algorithms.SimulatedAnnealing;
-using HeuristicLab.Algorithms.TabuSearch;
-using HeuristicLab.Algorithms.VariableNeighborhoodSearch;
-using HeuristicLab.Data;
-using HeuristicLab.Encodings.BinaryVectorEncoding;
-using HeuristicLab.Encodings.IntegerVectorEncoding;
-using HeuristicLab.Encodings.PermutationEncoding;
-using HeuristicLab.Encodings.RealVectorEncoding;
-using HeuristicLab.Encodings.ScheduleEncoding.JobSequenceMatrix;
-using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
-using HeuristicLab.Optimization;
-using HeuristicLab.Optimization.Operators;
-using HeuristicLab.Persistence.Default.Xml;
-using HeuristicLab.Problems.ArtificialAnt;
-using HeuristicLab.Problems.DataAnalysis;
-using HeuristicLab.Problems.DataAnalysis.Symbolic;
-using HeuristicLab.Problems.DataAnalysis.Symbolic.Classification;
-using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
-using HeuristicLab.Problems.Instances;
-using HeuristicLab.Problems.Instances.DataAnalysis;
-using HeuristicLab.Problems.Instances.TSPLIB;
-using HeuristicLab.Problems.Instances.VehicleRouting;
-using HeuristicLab.Problems.Knapsack;
-using HeuristicLab.Problems.Scheduling;
-using HeuristicLab.Problems.TestFunctions;
-using HeuristicLab.Problems.TravelingSalesman;
-using HeuristicLab.Problems.VehicleRouting;
-using HeuristicLab.Problems.VehicleRouting.Encodings.General;
-using HeuristicLab.Problems.VehicleRouting.Encodings.Potvin;
-using HeuristicLab.Problems.VehicleRouting.ProblemInstances;
-using HeuristicLab.Selection;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
-using StdDevStrategyVectorCreator = HeuristicLab.Encodings.RealVectorEncoding.StdDevStrategyVectorCreator;
-using StdDevStrategyVectorCrossover = HeuristicLab.Encodings.RealVectorEncoding.StdDevStrategyVectorCrossover;
-using StdDevStrategyVectorManipulator = HeuristicLab.Encodings.RealVectorEncoding.StdDevStrategyVectorManipulator;
-
-
-namespace HeuristicLab.Tests {
-  [TestClass]
-  public class SamplesTest {
-    private const string samplesDirectory = @"Samples\";
-
-    [ClassInitialize]
-    public static void MyClassInitialize(TestContext testContext) {
-      if (!Directory.Exists(samplesDirectory))
-        Directory.CreateDirectory(samplesDirectory);
-    }
-
-    #region GA
-    #region TSP
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateGaTspSampleTest() {
-      var ga = CreateGaTspSample();
-      XmlGenerator.Serialize(ga, @"Samples\GA_TSP.hl");
-    }
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunGaTspSampleTest() {
-      var ga = CreateGaTspSample();
-      ga.SetSeedRandomly.Value = false;
-      RunAlgorithm(ga);
-      Assert.AreEqual(12332, GetDoubleResult(ga, "BestQuality"));
-      Assert.AreEqual(13123.2, GetDoubleResult(ga, "CurrentAverageQuality"));
-      Assert.AreEqual(14538, GetDoubleResult(ga, "CurrentWorstQuality"));
-      Assert.AreEqual(99100, GetIntResult(ga, "EvaluatedSolutions"));
-    }
-
-    private GeneticAlgorithm CreateGaTspSample() {
-      GeneticAlgorithm ga = new GeneticAlgorithm();
-      #region Problem Configuration
-      var provider = new TSPLIBTSPInstanceProvider();
-      var instance = provider.GetDataDescriptors().Where(x => x.Name == "ch130").Single();
-      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
-      tspProblem.Load(provider.LoadData(instance));
-      tspProblem.UseDistanceMatrix.Value = true;
-      #endregion
-      #region Algorithm Configuration
-      ga.Name = "Genetic Algorithm - TSP";
-      ga.Description = "A genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
-      ga.Problem = tspProblem;
-      ConfigureGeneticAlgorithmParameters<ProportionalSelector, OrderCrossover2, InversionManipulator>(
-        ga, 100, 1, 1000, 0.05);
-      #endregion
-      return ga;
-    }
-    #endregion
-    #region VRP
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateGaVrpSampleTest() {
-      var ga = CreateGaVrpSample();
-      XmlGenerator.Serialize(ga, @"Samples\GA_VRP.hl");
-    }
-
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunGaVrpSampleTest() {
-      var ga = CreateGaVrpSample();
-      ga.SetSeedRandomly.Value = false;
-      RunAlgorithm(ga);
-      Assert.AreEqual(1828.9368669428338, GetDoubleResult(ga, "BestQuality"));
-      Assert.AreEqual(1830.1444308908331, GetDoubleResult(ga, "CurrentAverageQuality"));
-      Assert.AreEqual(1871.7128510304112, GetDoubleResult(ga, "CurrentWorstQuality"));
-      Assert.AreEqual(99100, GetIntResult(ga, "EvaluatedSolutions"));
-    }
-
-    private GeneticAlgorithm CreateGaVrpSample() {
-      GeneticAlgorithm ga = new GeneticAlgorithm();
-      #region Problem Configuration
-      VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
-
-      SolomonFormatInstanceProvider instanceProvider = new SolomonInstanceProvider();
-      CVRPTWData data = instanceProvider.Import(@"Test Resources\C101.txt", @"Test Resources\C101.opt.txt") as CVRPTWData;
-      vrpProblem.Load(data);
-      vrpProblem.Name = "C101 VRP (imported from Solomon)";
-      vrpProblem.Description = "Represents a Vehicle Routing Problem.";
-      CVRPTWProblemInstance instance = vrpProblem.ProblemInstance as CVRPTWProblemInstance;
-      instance.DistanceFactor.Value = 1;
-      instance.FleetUsageFactor.Value = 100;
-      instance.OverloadPenalty.Value = 100;
-      instance.TardinessPenalty.Value = 100;
-      instance.TimeFactor.Value = 0;
-      vrpProblem.MaximizationParameter.Value.Value = false;
-      instance.UseDistanceMatrix.Value = true;
-      instance.Vehicles.Value = 25;
-      #endregion
-      #region Algorithm Configuration
-      ga.Name = "Genetic Algorithm - VRP";
-      ga.Description = "A genetic algorithm which solves the \"C101\" vehicle routing problem (imported from Solomon)";
-      ga.Problem = vrpProblem;
-      ConfigureGeneticAlgorithmParameters<TournamentSelector, MultiVRPSolutionCrossover, MultiVRPSolutionManipulator>(
-        ga, 100, 1, 1000, 0.05, 3);
-
-      var xOver = (MultiVRPSolutionCrossover)ga.Crossover;
-      foreach (var op in xOver.Operators) {
-        xOver.Operators.SetItemCheckedState(op, false);
-      }
-      xOver.Operators.SetItemCheckedState(xOver.Operators
-        .OfType<PotvinRouteBasedCrossover>()
-        .Single(), true);
-      xOver.Operators.SetItemCheckedState(xOver.Operators
-        .OfType<PotvinSequenceBasedCrossover>()
-        .Single(), true);
-
-      var manipulator = (MultiVRPSolutionManipulator)ga.Mutator;
-      foreach (var op in manipulator.Operators) {
-        manipulator.Operators.SetItemCheckedState(op, false);
-      }
-      manipulator.Operators.SetItemCheckedState(manipulator.Operators
-        .OfType<PotvinOneLevelExchangeMainpulator>()
-        .Single(), true);
-      manipulator.Operators.SetItemCheckedState(manipulator.Operators
-        .OfType<PotvinTwoLevelExchangeManipulator>()
-        .Single(), true);
-      #endregion
-      return ga;
-    }
-    #endregion
-    #region ArtificialAnt
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateGpArtificialAntSampleTest() {
-      var ga = CreateGpArtificialAntSample();
-      XmlGenerator.Serialize(ga, @"Samples\SGP_SantaFe.hl");
-    }
-
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunGpArtificialAntSampleTest() {
-      var ga = CreateGpArtificialAntSample();
-      ga.SetSeedRandomly.Value = false;
-      RunAlgorithm(ga);
-      Assert.AreEqual(81, GetDoubleResult(ga, "BestQuality"));
-      Assert.AreEqual(48.19, GetDoubleResult(ga, "CurrentAverageQuality"));
-      Assert.AreEqual(0, GetDoubleResult(ga, "CurrentWorstQuality"));
-      Assert.AreEqual(50950, GetIntResult(ga, "EvaluatedSolutions"));
-    }
-
-    public GeneticAlgorithm CreateGpArtificialAntSample() {
-      GeneticAlgorithm ga = new GeneticAlgorithm();
-      #region Problem Configuration
-      ArtificialAntProblem antProblem = new ArtificialAntProblem();
-      antProblem.BestKnownQuality.Value = 89;
-      antProblem.MaxExpressionDepth.Value = 10;
-      antProblem.MaxExpressionLength.Value = 100;
-      antProblem.MaxFunctionArguments.Value = 3;
-      antProblem.MaxFunctionDefinitions.Value = 3;
-      antProblem.MaxTimeSteps.Value = 600;
-      #endregion
-      #region Algorithm Configuration
-      ga.Name = "Genetic Programming - Artificial Ant";
-      ga.Description = "A standard genetic programming algorithm to solve the artificial ant problem (Santa-Fe trail)";
-      ga.Problem = antProblem;
-      ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeArchitectureManipulator>(
-        ga, 1000, 1, 50, 0.15, 5);
-      var mutator = (MultiSymbolicExpressionTreeArchitectureManipulator)ga.Mutator;
-      mutator.Operators.SetItemCheckedState(mutator.Operators
-        .OfType<FullTreeShaker>()
-        .Single(), false);
-      mutator.Operators.SetItemCheckedState(mutator.Operators
-        .OfType<OnePointShaker>()
-        .Single(), false);
-      mutator.Operators.SetItemCheckedState(mutator.Operators
-        .OfType<ArgumentDeleter>()
-        .Single(), false);
-      mutator.Operators.SetItemCheckedState(mutator.Operators
-        .OfType<SubroutineDeleter>()
-        .Single(), false);
-      #endregion
-      return ga;
-    }
-    #endregion
-    #region Symbolic Regression
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateGpSymbolicRegressionSampleTest() {
-      var ga = CreateGpSymbolicRegressionSample();
-      XmlGenerator.Serialize(ga, @"Samples\SGP_SymbReg.hl");
-    }
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunGpSymbolicRegressionSampleTest() {
-      var ga = CreateGpSymbolicRegressionSample();
-      ga.SetSeedRandomly.Value = false;
-      RunAlgorithm(ga);
-      Assert.AreEqual(0.858344291534625, GetDoubleResult(ga, "BestQuality"), 1E-8);
-      Assert.AreEqual(0.56758466520692641, GetDoubleResult(ga, "CurrentAverageQuality"), 1E-8);
-      Assert.AreEqual(0, GetDoubleResult(ga, "CurrentWorstQuality"), 1E-8);
-      Assert.AreEqual(50950, GetIntResult(ga, "EvaluatedSolutions"));
-      var bestTrainingSolution = (IRegressionSolution)ga.Results["Best training solution"].Value;
-      Assert.AreEqual(0.85504801557844745, bestTrainingSolution.TrainingRSquared, 1E-8);
-      Assert.AreEqual(0.86259381948647817, bestTrainingSolution.TestRSquared, 1E-8);
-      var bestValidationSolution = (IRegressionSolution)ga.Results["Best validation solution"].Value;
-      Assert.AreEqual(0.84854338315539746, bestValidationSolution.TrainingRSquared, 1E-8);
-      Assert.AreEqual(0.8662813452656678, bestValidationSolution.TestRSquared, 1E-8);
-    }
-
-    private GeneticAlgorithm CreateGpSymbolicRegressionSample() {
-      GeneticAlgorithm ga = new GeneticAlgorithm();
-      #region Problem Configuration
-      SymbolicRegressionSingleObjectiveProblem symbRegProblem = new SymbolicRegressionSingleObjectiveProblem();
-      symbRegProblem.Name = "Tower Symbolic Regression Problem";
-      symbRegProblem.Description = "Tower Dataset (downloaded from: http://www.symbolicregression.com/?q=towerProblem)";
-      RegressionRealWorldInstanceProvider provider = new RegressionRealWorldInstanceProvider();
-      var instance = provider.GetDataDescriptors().Where(x => x.Name.Equals("Tower")).Single();
-      var towerProblemData = (RegressionProblemData)provider.LoadData(instance);
-      towerProblemData.TargetVariableParameter.Value = towerProblemData.TargetVariableParameter.ValidValues
-        .First(v => v.Value == "towerResponse");
-      towerProblemData.InputVariables.SetItemCheckedState(
-        towerProblemData.InputVariables.Single(x => x.Value == "x1"), true);
-      towerProblemData.InputVariables.SetItemCheckedState(
-        towerProblemData.InputVariables.Single(x => x.Value == "x7"), false);
-      towerProblemData.InputVariables.SetItemCheckedState(
-        towerProblemData.InputVariables.Single(x => x.Value == "x11"), false);
-      towerProblemData.InputVariables.SetItemCheckedState(
-        towerProblemData.InputVariables.Single(x => x.Value == "x16"), false);
-      towerProblemData.InputVariables.SetItemCheckedState(
-        towerProblemData.InputVariables.Single(x => x.Value == "x21"), false);
-      towerProblemData.InputVariables.SetItemCheckedState(
-        towerProblemData.InputVariables.Single(x => x.Value == "x25"), false);
-      towerProblemData.InputVariables.SetItemCheckedState(
-        towerProblemData.InputVariables.Single(x => x.Value == "towerResponse"), false);
-      towerProblemData.TrainingPartition.Start = 0;
-      towerProblemData.TrainingPartition.End = 3136;
-      towerProblemData.TestPartition.Start = 3136;
-      towerProblemData.TestPartition.End = 4999;
-      towerProblemData.Name = "Data imported from towerData.txt";
-      towerProblemData.Description = "Chemical concentration at top of distillation tower, dataset downloaded from: http://vanillamodeling.com/realproblems.html, best R² achieved with nu-SVR = 0.97";
-      symbRegProblem.ProblemData = towerProblemData;
-
-      // configure grammar
-      var grammar = new TypeCoherentExpressionGrammar();
-      grammar.ConfigureAsDefaultRegressionGrammar();
-      grammar.Symbols.OfType<VariableCondition>().Single().InitialFrequency = 0.0;
-      var varSymbol = grammar.Symbols.OfType<Variable>().Where(x => !(x is LaggedVariable)).Single();
-      varSymbol.WeightMu = 1.0;
-      varSymbol.WeightSigma = 1.0;
-      varSymbol.WeightManipulatorMu = 0.0;
-      varSymbol.WeightManipulatorSigma = 0.05;
-      varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
-      var constSymbol = grammar.Symbols.OfType<Constant>().Single();
-      constSymbol.MaxValue = 20;
-      constSymbol.MinValue = -20;
-      constSymbol.ManipulatorMu = 0.0;
-      constSymbol.ManipulatorSigma = 1;
-      constSymbol.MultiplicativeManipulatorSigma = 0.03;
-      symbRegProblem.SymbolicExpressionTreeGrammar = grammar;
-
-      // configure remaining problem parameters
-      symbRegProblem.BestKnownQuality.Value = 0.97;
-      symbRegProblem.FitnessCalculationPartition.Start = 0;
-      symbRegProblem.FitnessCalculationPartition.End = 2300;
-      symbRegProblem.ValidationPartition.Start = 2300;
-      symbRegProblem.ValidationPartition.End = 3136;
-      symbRegProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
-      symbRegProblem.MaximumSymbolicExpressionTreeLength.Value = 150;
-      symbRegProblem.MaximumSymbolicExpressionTreeDepth.Value = 12;
-      symbRegProblem.MaximumFunctionDefinitions.Value = 0;
-      symbRegProblem.MaximumFunctionArguments.Value = 0;
-
-      symbRegProblem.EvaluatorParameter.Value = new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator();
-      #endregion
-      #region Algorithm Configuration
-      ga.Problem = symbRegProblem;
-      ga.Name = "Genetic Programming - Symbolic Regression";
-      ga.Description = "A standard genetic programming algorithm to solve a symbolic regression problem (tower dataset)";
-      ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
-        ga, 1000, 1, 50, 0.15, 5);
-      var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
-      mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
-      mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
-
-      ga.Analyzer.Operators.SetItemCheckedState(
-        ga.Analyzer.Operators
-        .OfType<SymbolicRegressionSingleObjectiveOverfittingAnalyzer>()
-        .Single(), false);
-      ga.Analyzer.Operators.SetItemCheckedState(
-        ga.Analyzer.Operators
-        .OfType<SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
-        .First(), false);
-      #endregion
-      return ga;
-    }
-    #endregion
-    #region Symbolic Classification
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateGpSymbolicClassificationSampleTest() {
-      var ga = CreateGpSymbolicClassificationSample();
-      XmlGenerator.Serialize(ga, @"Samples\SGP_SymbClass.hl");
-    }
-
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunGpSymbolicClassificationSampleTest() {
-      var ga = CreateGpSymbolicClassificationSample();
-      ga.SetSeedRandomly.Value = false;
-      RunAlgorithm(ga);
-      Assert.AreEqual(0.141880203907627, GetDoubleResult(ga, "BestQuality"), 1E-8);
-      Assert.AreEqual(4.3246992327753295, GetDoubleResult(ga, "CurrentAverageQuality"), 1E-8);
-      Assert.AreEqual(100.62175156249987, GetDoubleResult(ga, "CurrentWorstQuality"), 1E-8);
-      Assert.AreEqual(100900, GetIntResult(ga, "EvaluatedSolutions"));
-      var bestTrainingSolution = (IClassificationSolution)ga.Results["Best training solution"].Value;
-      Assert.AreEqual(0.80875, bestTrainingSolution.TrainingAccuracy, 1E-8);
-      Assert.AreEqual(0.795031055900621, bestTrainingSolution.TestAccuracy, 1E-8);
-      var bestValidationSolution = (IClassificationSolution)ga.Results["Best validation solution"].Value;
-      Assert.AreEqual(0.81375, bestValidationSolution.TrainingAccuracy, 1E-8);
-      Assert.AreEqual(0.788819875776398, bestValidationSolution.TestAccuracy, 1E-8);
-    }
-
-    private GeneticAlgorithm CreateGpSymbolicClassificationSample() {
-      GeneticAlgorithm ga = new GeneticAlgorithm();
-      #region Problem Configuration
-      SymbolicClassificationSingleObjectiveProblem symbClassProblem = new SymbolicClassificationSingleObjectiveProblem();
-      symbClassProblem.Name = "Mammography Classification Problem";
-      symbClassProblem.Description = "Mammography dataset imported from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass)";
-      UCIInstanceProvider provider = new UCIInstanceProvider();
-      var instance = provider.GetDataDescriptors().Where(x => x.Name.Equals("Mammography, M. Elter, 2007")).Single();
-      var mammoData = (ClassificationProblemData)provider.LoadData(instance);
-      mammoData.TargetVariableParameter.Value = mammoData.TargetVariableParameter.ValidValues
-        .First(v => v.Value == "Severity");
-      mammoData.InputVariables.SetItemCheckedState(
-        mammoData.InputVariables.Single(x => x.Value == "BI-RADS"), false);
-      mammoData.InputVariables.SetItemCheckedState(
-        mammoData.InputVariables.Single(x => x.Value == "Age"), true);
-      mammoData.InputVariables.SetItemCheckedState(
-        mammoData.InputVariables.Single(x => x.Value == "Shape"), true);
-      mammoData.InputVariables.SetItemCheckedState(
-        mammoData.InputVariables.Single(x => x.Value == "Margin"), true);
-      mammoData.InputVariables.SetItemCheckedState(
-        mammoData.InputVariables.Single(x => x.Value == "Density"), true);
-      mammoData.InputVariables.SetItemCheckedState(
-        mammoData.InputVariables.Single(x => x.Value == "Severity"), false);
-      mammoData.TrainingPartition.Start = 0;
-      mammoData.TrainingPartition.End = 800;
-      mammoData.TestPartition.Start = 800;
-      mammoData.TestPartition.End = 961;
-      mammoData.Name = "Data imported from mammographic_masses.csv";
-      mammoData.Description = "Original dataset: http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass, missing values have been replaced with median values.";
-      symbClassProblem.ProblemData = mammoData;
-
-      // configure grammar
-      var grammar = new TypeCoherentExpressionGrammar();
-      grammar.ConfigureAsDefaultClassificationGrammar();
-      grammar.Symbols.OfType<VariableCondition>().Single().Enabled = false;
-      var varSymbol = grammar.Symbols.OfType<Variable>().Where(x => !(x is LaggedVariable)).Single();
-      varSymbol.WeightMu = 1.0;
-      varSymbol.WeightSigma = 1.0;
-      varSymbol.WeightManipulatorMu = 0.0;
-      varSymbol.WeightManipulatorSigma = 0.05;
-      varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
-      var constSymbol = grammar.Symbols.OfType<Constant>().Single();
-      constSymbol.MaxValue = 20;
-      constSymbol.MinValue = -20;
-      constSymbol.ManipulatorMu = 0.0;
-      constSymbol.ManipulatorSigma = 1;
-      constSymbol.MultiplicativeManipulatorSigma = 0.03;
-      symbClassProblem.SymbolicExpressionTreeGrammar = grammar;
-
-      // configure remaining problem parameters
-      symbClassProblem.BestKnownQuality.Value = 0.0;
-      symbClassProblem.FitnessCalculationPartition.Start = 0;
-      symbClassProblem.FitnessCalculationPartition.End = 400;
-      symbClassProblem.ValidationPartition.Start = 400;
-      symbClassProblem.ValidationPartition.End = 800;
-      symbClassProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
-      symbClassProblem.MaximumSymbolicExpressionTreeLength.Value = 100;
-      symbClassProblem.MaximumSymbolicExpressionTreeDepth.Value = 10;
-      symbClassProblem.MaximumFunctionDefinitions.Value = 0;
-      symbClassProblem.MaximumFunctionArguments.Value = 0;
-      symbClassProblem.EvaluatorParameter.Value = new SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator();
-      #endregion
-      #region Algorithm Configuration
-      ga.Problem = symbClassProblem;
-      ga.Name = "Genetic Programming - Symbolic Classification";
-      ga.Description = "A standard genetic programming algorithm to solve a classification problem (Mammographic+Mass dataset)";
-      ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
-        ga, 1000, 1, 100, 0.15, 5
-        );
-
-      var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
-      mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
-      mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
-
-      ga.Analyzer.Operators.SetItemCheckedState(
-        ga.Analyzer.Operators
-        .OfType<SymbolicClassificationSingleObjectiveOverfittingAnalyzer>()
-        .Single(), false);
-      ga.Analyzer.Operators.SetItemCheckedState(
-        ga.Analyzer.Operators
-        .OfType<SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
-        .First(), false);
-      #endregion
-      return ga;
-    }
-    #endregion
-    #region LawnMower
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunGpLawnMowerSampleTest() {
-      var ga = CreateGpLawnMowerSample();
-      ga.SetSeedRandomly.Value = false;
-      RunAlgorithm(ga);
-    }
-
-    public GeneticAlgorithm CreateGpLawnMowerSample() {
-      GeneticAlgorithm ga = new GeneticAlgorithm();
-      #region Problem Configuration
-      var problem = new HeuristicLab.Problems.LawnMower.Problem();
-      #endregion
-      #region Algorithm Configuration
-      ga.Name = "Genetic Programming - Lawn Mower";
-      ga.Description = "A standard genetic programming algorithm to solve the lawn mower problem";
-      ga.Problem = problem;
-      ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeArchitectureManipulator>(
-        ga, 1000, 1, 50, 0.25, 5);
-      var mutator = (MultiSymbolicExpressionTreeArchitectureManipulator)ga.Mutator;
-      mutator.Operators.SetItemCheckedState(mutator.Operators
-        .OfType<OnePointShaker>()
-        .Single(), false);
-      #endregion
-      return ga;
-    }
-    #endregion
-    #endregion
-
-    #region ES
-    #region Griewank
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateEsGriewankSampleTest() {
-      var es = CreateEsGriewankSample();
-      XmlGenerator.Serialize(es, @"Samples\ES_Griewank.hl");
-    }
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunEsGriewankSampleTest() {
-      var es = CreateEsGriewankSample();
-      es.SetSeedRandomly.Value = false;
-      RunAlgorithm(es);
-      Assert.AreEqual(0, GetDoubleResult(es, "BestQuality"));
-      Assert.AreEqual(0, GetDoubleResult(es, "CurrentAverageQuality"));
-      Assert.AreEqual(0, GetDoubleResult(es, "CurrentWorstQuality"));
-      Assert.AreEqual(100020, GetIntResult(es, "EvaluatedSolutions"));
-    }
-
-    private EvolutionStrategy CreateEsGriewankSample() {
-      EvolutionStrategy es = new EvolutionStrategy();
-      #region Problem Configuration
-      SingleObjectiveTestFunctionProblem problem = new SingleObjectiveTestFunctionProblem();
-
-      problem.ProblemSize.Value = 10;
-      problem.EvaluatorParameter.Value = new GriewankEvaluator();
-      problem.SolutionCreatorParameter.Value = new UniformRandomRealVectorCreator();
-      problem.Maximization.Value = false;
-      problem.Bounds = new DoubleMatrix(new double[,] { { -600, 600 } });
-      problem.BestKnownQuality.Value = 0;
-      problem.BestKnownSolutionParameter.Value = new RealVector(10);
-      problem.Name = "Single Objective Test Function";
-      problem.Description = "Test function with real valued inputs and a single objective.";
-      #endregion
-      #region Algorithm Configuration
-      es.Name = "Evolution Strategy - Griewank";
-      es.Description = "An evolution strategy which solves the 10-dimensional Griewank test function";
-      es.Problem = problem;
-      ConfigureEvolutionStrategyParameters<AverageCrossover, NormalAllPositionsManipulator,
-        StdDevStrategyVectorCreator, StdDevStrategyVectorCrossover, StdDevStrategyVectorManipulator>(
-        es, 20, 500, 2, 200, false);
-
-      StdDevStrategyVectorCreator strategyCreator = (StdDevStrategyVectorCreator)es.StrategyParameterCreator;
-      strategyCreator.BoundsParameter.Value = new DoubleMatrix(new double[,] { { 1, 20 } });
-
-      StdDevStrategyVectorManipulator strategyManipulator = (StdDevStrategyVectorManipulator)es.StrategyParameterManipulator;
-      strategyManipulator.BoundsParameter.Value = new DoubleMatrix(new double[,] { { 1E-12, 30 } });
-      strategyManipulator.GeneralLearningRateParameter.Value = new DoubleValue(0.22360679774997896);
-      strategyManipulator.LearningRateParameter.Value = new DoubleValue(0.39763536438352531);
-      #endregion
-      return es;
-    }
-    #endregion
-    #endregion
-
-    #region Island GA
-    #region TSP
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateIslandGaTspSampleTest() {
-      var ga = CreateIslandGaTspSample();
-      XmlGenerator.Serialize(ga, @"Samples\IslandGA_TSP.hl");
-    }
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunIslandGaTspSampleTest() {
-      var ga = CreateIslandGaTspSample();
-      ga.SetSeedRandomly.Value = false;
-      RunAlgorithm(ga);
-      Assert.AreEqual(9918, GetDoubleResult(ga, "BestQuality"));
-      Assert.AreEqual(10324.64, GetDoubleResult(ga, "CurrentAverageQuality"));
-      Assert.AreEqual(11823, GetDoubleResult(ga, "CurrentWorstQuality"));
-      Assert.AreEqual(495500, GetIntResult(ga, "EvaluatedSolutions"));
-    }
-
-    private IslandGeneticAlgorithm CreateIslandGaTspSample() {
-      IslandGeneticAlgorithm ga = new IslandGeneticAlgorithm();
-      #region Problem Configuration
-      var provider = new TSPLIBTSPInstanceProvider();
-      var instance = provider.GetDataDescriptors().Where(x => x.Name == "ch130").Single();
-      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
-      tspProblem.Load(provider.LoadData(instance));
-      tspProblem.UseDistanceMatrix.Value = true;
-      #endregion
-      #region Algorithm Configuration
-      ga.Name = "Island Genetic Algorithm - TSP";
-      ga.Description = "An island genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
-      ga.Problem = tspProblem;
-      ConfigureIslandGeneticAlgorithmParameters<ProportionalSelector, OrderCrossover2, InversionManipulator,
-        UnidirectionalRingMigrator, BestSelector, WorstReplacer>(
-        ga, 100, 1, 1000, 0.05, 5, 50, 0.25);
-      #endregion
-      return ga;
-    }
-    #endregion
-    #endregion
-
-    #region LS
-    #region Knapsack
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateLocalSearchKnapsackSampleTest() {
-      var ls = CreateLocalSearchKnapsackSample();
-      XmlGenerator.Serialize(ls, @"Samples\LS_Knapsack.hl");
-    }
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "medium")]
-    public void RunLocalSearchKnapsackSampleTest() {
-      var ls = CreateLocalSearchKnapsackSample();
-      ls.SetSeedRandomly.Value = false;
-      RunAlgorithm(ls);
-      Assert.AreEqual(345, GetDoubleResult(ls, "BestQuality"));
-      Assert.AreEqual(340.70731707317071, GetDoubleResult(ls, "CurrentAverageQuality"));
-      Assert.AreEqual(337, GetDoubleResult(ls, "CurrentWorstQuality"));
-      Assert.AreEqual(82000, GetIntResult(ls, "EvaluatedMoves"));
-    }
-
-    private LocalSearch CreateLocalSearchKnapsackSample() {
-      LocalSearch ls = new LocalSearch();
-      #region Problem Configuration
-      KnapsackProblem problem = new KnapsackProblem();
-      problem.BestKnownQuality = new DoubleValue(362);
-      problem.BestKnownSolution = new HeuristicLab.Encodings.BinaryVectorEncoding.BinaryVector(new bool[] { 
-       true , false, false, true , true , true , true , true , false, true , true , true , true , true , true , false, true , false, true , true , false, true , true , false, true , false, true , true , true , false, true , true , false, true , true , false, true , false, true , true , true , true , true , true , true , true , true , true , true , true , true , false, true , false, false, true , true , false, true , true , true , true , true , true , true , true , false, true , false, true , true , true , true , false, true , true , true , true , true , true , true , true});
-      problem.EvaluatorParameter.Value = new KnapsackEvaluator();
-      problem.SolutionCreatorParameter.Value = new RandomBinaryVectorCreator();
-      problem.KnapsackCapacity.Value = 297;
-      problem.Maximization.Value = true;
-      problem.Penalty.Value = 1;
-      problem.Values = new IntArray(new int[] { 
-  6, 1, 1, 6, 7, 8, 7, 4, 2, 5, 2, 6, 7, 8, 7, 1, 7, 1, 9, 4, 2, 6, 5,  3, 5, 3, 3, 6, 5, 2, 4, 9, 4, 5, 7, 1, 4, 3, 5, 5, 8, 3, 6, 7, 3, 9, 7, 7, 5, 5, 7, 1, 4, 4, 3, 9, 5, 1, 6, 2, 2, 6, 1, 6, 5, 4, 4, 7, 1,  8, 9, 9, 7, 4, 3, 8, 7, 5, 7, 4, 4, 5});
-      problem.Weights = new IntArray(new int[] { 
- 1, 9, 3, 6, 5, 3, 8, 1, 7, 4, 2, 1, 2, 7, 9, 9, 8, 4, 9, 2, 4, 8, 3, 7, 5, 7, 5, 5, 1, 9, 8, 7, 8, 9, 1, 3, 3, 8, 8, 5, 1, 2, 4, 3, 6, 9, 4, 4, 9, 7, 4, 5, 1, 9, 7, 6, 7, 4, 7, 1, 2, 1, 2, 9, 8, 6, 8, 4, 7, 6, 7, 5, 3, 9, 4, 7, 4, 6, 1, 2, 5, 4});
-      problem.Name = "Knapsack Problem";
-      problem.Description = "Represents a Knapsack problem.";
-      #endregion
-      #region Algorithm Configuration
-      ls.Name = "Local Search - Knapsack";
-      ls.Description = "A local search algorithm that solves a randomly generated Knapsack problem";
-      ls.Problem = problem;
-      ls.MaximumIterations.Value = 1000;
-      ls.MoveEvaluator = ls.MoveEvaluatorParameter.ValidValues
-        .OfType<KnapsackOneBitflipMoveEvaluator>()
-        .Single();
-      ls.MoveGenerator = ls.MoveGeneratorParameter.ValidValues
-        .OfType<ExhaustiveOneBitflipMoveGenerator>()
-        .Single();
-      ls.MoveMaker = ls.MoveMakerParameter.ValidValues
-        .OfType<OneBitflipMoveMaker>()
-        .Single();
-      ls.SampleSize.Value = 100;
-      ls.Seed.Value = 0;
-      ls.SetSeedRandomly.Value = true;
-      #endregion
-      ls.Engine = new ParallelEngine.ParallelEngine();
-      return ls;
-    }
-    #endregion
-    #endregion
-
-    #region PSO
-    #region Schwefel
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreatePsoSchwefelSampleTest() {
-      var pso = CreatePsoSchwefelSample();
-      XmlGenerator.Serialize(pso, @"Samples\PSO_Schwefel.hl");
-    }
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "medium")]
-    public void RunPsoSchwefelSampleTest() {
-      var pso = CreatePsoSchwefelSample();
-      pso.SetSeedRandomly.Value = false;
-      RunAlgorithm(pso);
-      if (!Environment.Is64BitProcess) {
-        Assert.AreEqual(118.44027985932837, GetDoubleResult(pso, "BestQuality"));
-        Assert.AreEqual(140.71570105946438, GetDoubleResult(pso, "CurrentAverageQuality"));
-        Assert.AreEqual(220.956806502853, GetDoubleResult(pso, "CurrentWorstQuality"));
-        Assert.AreEqual(1000, GetIntResult(pso, "Iterations"));
-      } else {
-        Assert.AreEqual(118.43958282879345, GetDoubleResult(pso, "BestQuality"));
-        Assert.AreEqual(139.43946864779372, GetDoubleResult(pso, "CurrentAverageQuality"));
-        Assert.AreEqual(217.14654589055152, GetDoubleResult(pso, "CurrentWorstQuality"));
-        Assert.AreEqual(1000, GetIntResult(pso, "Iterations"));
-      }
-    }
-    private ParticleSwarmOptimization CreatePsoSchwefelSample() {
-      ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
-      #region Problem Configuration
-      var problem = new SingleObjectiveTestFunctionProblem();
-      problem.BestKnownQuality.Value = 0.0;
-      problem.BestKnownSolutionParameter.Value = new RealVector(new double[] { 420.968746, 420.968746 });
-      problem.Bounds = new DoubleMatrix(new double[,] { { -500, 500 } });
-      problem.EvaluatorParameter.Value = new SchwefelEvaluator();
-      problem.Maximization.Value = false;
-      problem.ProblemSize.Value = 2;
-      problem.SolutionCreatorParameter.Value = new UniformRandomRealVectorCreator();
-      #endregion
-      #region Algorithm Configuration
-      pso.Name = "Particle Swarm Optimization - Schwefel";
-      pso.Description = "A particle swarm optimization algorithm which solves the 2-dimensional Schwefel test function (based on the description in Pedersen, M.E.H. (2010). PhD thesis. University of Southampton)";
-      pso.Problem = problem;
-      pso.Inertia.Value = 10;
-      pso.MaxIterations.Value = 1000;
-      pso.NeighborBestAttraction.Value = 0.5;
-      pso.PersonalBestAttraction.Value = -0.01;
-      pso.SwarmSize.Value = 50;
-
-      var inertiaUpdater = pso.InertiaUpdaterParameter.ValidValues
-        .OfType<ExponentialDiscreteDoubleValueModifier>()
-        .Single();
-      inertiaUpdater.StartValueParameter.Value = new DoubleValue(10);
-      inertiaUpdater.EndValueParameter.Value = new DoubleValue(1);
-      pso.InertiaUpdater = inertiaUpdater;
-
-      pso.ParticleCreator = pso.ParticleCreatorParameter.ValidValues
-        .OfType<RealVectorParticleCreator>()
-        .Single();
-      var swarmUpdater = pso.SwarmUpdaterParameter.ValidValues
-        .OfType<RealVectorSwarmUpdater>()
-        .Single();
-      swarmUpdater.VelocityBoundsIndexParameter.ActualName = "Iterations";
-      swarmUpdater.VelocityBoundsParameter.Value = new DoubleMatrix(new double[,] { { -10, 10 } });
-      swarmUpdater.VelocityBoundsStartValueParameter.Value = new DoubleValue(10.0);
-      swarmUpdater.VelocityBoundsEndValueParameter.Value = new DoubleValue(1.0);
-      swarmUpdater.VelocityBoundsScalingOperatorParameter.Value = swarmUpdater.VelocityBoundsScalingOperatorParameter.ValidValues
-        .OfType<ExponentialDiscreteDoubleValueModifier>()
-        .Single();
-
-      pso.TopologyInitializer = null;
-      pso.TopologyUpdater = null;
-      pso.SwarmUpdater = swarmUpdater;
-      pso.Seed.Value = 0;
-      pso.SetSeedRandomly.Value = true;
-      #endregion
-      pso.Engine = new ParallelEngine.ParallelEngine();
-      return pso;
-    }
-    #endregion
-    #endregion
-
-    #region SA
-    #region Rastrigin
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateSimulatedAnnealingRastriginSampleTest() {
-      var sa = CreateSimulatedAnnealingRastriginSample();
-      XmlGenerator.Serialize(sa, @"Samples\SA_Rastrigin.hl");
-    }
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "medium")]
-    public void RunSimulatedAnnealingRastriginSampleTest() {
-      var sa = CreateSimulatedAnnealingRastriginSample();
-      sa.SetSeedRandomly.Value = false;
-      RunAlgorithm(sa);
-      Assert.AreEqual(0.00014039606034543795, GetDoubleResult(sa, "BestQuality"));
-      Assert.AreEqual(5000, GetIntResult(sa, "EvaluatedMoves"));
-    }
-    private SimulatedAnnealing CreateSimulatedAnnealingRastriginSample() {
-      SimulatedAnnealing sa = new SimulatedAnnealing();
-      #region Problem Configuration
-      var problem = new SingleObjectiveTestFunctionProblem();
-      problem.BestKnownQuality.Value = 0.0;
-      problem.BestKnownSolutionParameter.Value = new RealVector(new double[] { 0, 0 });
-      problem.Bounds = new DoubleMatrix(new double[,] { { -5.12, 5.12 } });
-      problem.EvaluatorParameter.Value = new RastriginEvaluator();
-      problem.Maximization.Value = false;
-      problem.ProblemSize.Value = 2;
-      problem.SolutionCreatorParameter.Value = new UniformRandomRealVectorCreator();
-      #endregion
-      #region Algorithm Configuration
-      sa.Name = "Simulated Annealing - Rastrigin";
-      sa.Description = "A simulated annealing algorithm that solves the 2-dimensional Rastrigin test function";
-      sa.Problem = problem;
-      var annealingOperator = sa.AnnealingOperatorParameter.ValidValues
-        .OfType<ExponentialDiscreteDoubleValueModifier>()
-        .Single();
-      annealingOperator.StartIndexParameter.Value = new IntValue(0);
-      sa.AnnealingOperator = annealingOperator;
-
-      sa.EndTemperature.Value = 1E-6;
-      sa.InnerIterations.Value = 50;
-      sa.MaximumIterations.Value = 100;
-      var moveEvaluator = sa.MoveEvaluatorParameter.ValidValues
-        .OfType<RastriginAdditiveMoveEvaluator>()
-        .Single();
-      moveEvaluator.A.Value = 10;
-      sa.MoveEvaluator = moveEvaluator;
-
-      var moveGenerator = sa.MoveGeneratorParameter.ValidValues
-        .OfType<StochasticNormalMultiMoveGenerator>()
-        .Single();
-      moveGenerator.SigmaParameter.Value = new DoubleValue(1);
-      sa.MoveGenerator = moveGenerator;
-
-      sa.MoveMaker = sa.MoveMakerParameter.ValidValues
-        .OfType<AdditiveMoveMaker>()
-        .Single();
-
-      sa.Seed.Value = 0;
-      sa.SetSeedRandomly.Value = true;
-      sa.StartTemperature.Value = 1;
-      #endregion
-      sa.Engine = new ParallelEngine.ParallelEngine();
-      return sa;
-    }
-    #endregion
-    #endregion
-
-    #region TS
-    #region TSP
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateTabuSearchTspSampleTest() {
-      var ts = CreateTabuSearchTspSample();
-      XmlGenerator.Serialize(ts, @"Samples\TS_TSP.hl");
-    }
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunTabuSearchTspSampleTest() {
-      var ts = CreateTabuSearchTspSample();
-      ts.SetSeedRandomly.Value = false;
-      RunAlgorithm(ts);
-      Assert.AreEqual(6294, GetDoubleResult(ts, "BestQuality"));
-      Assert.AreEqual(7380.0386666666664, GetDoubleResult(ts, "CurrentAverageQuality"));
-      Assert.AreEqual(8328, GetDoubleResult(ts, "CurrentWorstQuality"));
-      Assert.AreEqual(750000, GetIntResult(ts, "EvaluatedMoves"));
-    }
-
-    private TabuSearch CreateTabuSearchTspSample() {
-      TabuSearch ts = new TabuSearch();
-      #region Problem Configuration
-      var provider = new TSPLIBTSPInstanceProvider();
-      var instance = provider.GetDataDescriptors().Where(x => x.Name == "ch130").Single();
-      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
-      tspProblem.Load(provider.LoadData(instance));
-      tspProblem.UseDistanceMatrix.Value = true;
-      #endregion
-      #region Algorithm Configuration
-      ts.Name = "Tabu Search - TSP";
-      ts.Description = "A tabu search algorithm that solves the \"ch130\" TSP (imported from TSPLIB)";
-      ts.Problem = tspProblem;
-
-      ts.MaximumIterations.Value = 1000;
-      // move generator has to be set first
-      var moveGenerator = ts.MoveGeneratorParameter.ValidValues
-        .OfType<StochasticInversionMultiMoveGenerator>()
-        .Single();
-      ts.MoveGenerator = moveGenerator;
-      var moveEvaluator = ts.MoveEvaluatorParameter.ValidValues
-        .OfType<TSPInversionMoveRoundedEuclideanPathEvaluator>()
-        .Single();
-      ts.MoveEvaluator = moveEvaluator;
-      var moveMaker = ts.MoveMakerParameter.ValidValues
-        .OfType<InversionMoveMaker>()
-        .Single();
-      ts.MoveMaker = moveMaker;
-      ts.SampleSize.Value = 750;
-      ts.Seed.Value = 0;
-      ts.SetSeedRandomly.Value = true;
-
-      var tabuChecker = ts.TabuCheckerParameter.ValidValues
-        .OfType<InversionMoveSoftTabuCriterion>()
-        .Single();
-      tabuChecker.UseAspirationCriterion.Value = true;
-      ts.TabuChecker = tabuChecker;
-
-      var tabuMaker = ts.TabuMakerParameter.ValidValues
-        .OfType<InversionMoveTabuMaker>()
-        .Single();
-      ts.TabuMaker = tabuMaker;
-      ts.TabuTenure.Value = 60;
-
-      #endregion
-      ts.Engine = new ParallelEngine.ParallelEngine();
-      return ts;
-    }
-    #endregion
-
-    #region VRP
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateTabuSearchVRPSampleTest() {
-      var vrp = CreateTabuSearchVrpSample();
-      XmlGenerator.Serialize(vrp, @"Samples\TS_VRP.hl");
-    }
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunTabuSearchVRPSampleTest() {
-      var vrp = CreateTabuSearchVrpSample();
-      vrp.SetSeedRandomly.Value = false;
-      RunAlgorithm(vrp);
-      Assert.AreEqual(1473, GetDoubleResult(vrp, "BestQuality"));
-      Assert.AreEqual(2102.1192622950812, GetDoubleResult(vrp, "CurrentAverageQuality"));
-      Assert.AreEqual(4006, GetDoubleResult(vrp, "CurrentWorstQuality"));
-      Assert.AreEqual(119072, GetIntResult(vrp, "EvaluatedMoves"));
-    }
-
-    private TabuSearch CreateTabuSearchVrpSample() {
-      TabuSearch ts = new TabuSearch();
-      #region Problem Configuration
-      var provider = new AugeratInstanceProvider();
-      var instance = provider.GetDataDescriptors().Where(x => x.Name == "A-n62-k8").Single();
-      VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
-      vrpProblem.Load(provider.LoadData(instance));
-      #endregion
-      #region Algorithm Configuration
-      ts.Name = "Tabu Search - VRP";
-      ts.Description = "A tabu search algorithm that solves the \"A-n62-k8\" VRP (imported from Augerat)";
-      ts.Problem = vrpProblem;
-
-      ts.MaximumIterations.Value = 200;
-      // move generator has to be set first
-      var moveGenerator = ts.MoveGeneratorParameter.ValidValues
-        .OfType<PotvinCustomerRelocationExhaustiveMoveGenerator>()
-        .Single();
-      ts.MoveGenerator = moveGenerator;
-      var moveEvaluator = ts.MoveEvaluatorParameter.ValidValues
-        .OfType<PotvinCustomerRelocationMoveEvaluator>()
-        .Single();
-      ts.MoveEvaluator = moveEvaluator;
-      var moveMaker = ts.MoveMakerParameter.ValidValues
-        .OfType<PotvinCustomerRelocationMoveMaker>()
-        .Single();
-      ts.MoveMaker = moveMaker;
-      ts.SampleSize.Value = 1000;
-      ts.Seed.Value = 0;
-      ts.SetSeedRandomly.Value = true;
-
-      var tabuChecker = ts.TabuCheckerParameter.ValidValues
-        .OfType<PotvinCustomerRelocationMoveTabuCriterion>()
-        .Single();
-      tabuChecker.UseAspirationCriterion.Value = false;
-      ts.TabuChecker = tabuChecker;
-
-      var tabuMaker = ts.TabuMakerParameter.ValidValues
-        .OfType<PotvinCustomerRelocationMoveTabuMaker>()
-        .Single();
-      ts.TabuMaker = tabuMaker;
-      ts.TabuTenure.Value = 6;
-
-      #endregion
-      ts.Engine = new ParallelEngine.ParallelEngine();
-      return ts;
-    }
-    #endregion
-    #endregion
-
-    #region VNS
-    #region TSP
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateVnsTspSampleTest() {
-      var vns = CreateVnsTspSample();
-      XmlGenerator.Serialize(vns, @"Samples\VNS_TSP.hl");
-    }
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunVnsTspSampleTest() {
-      var vns = CreateVnsTspSample();
-      vns.SetSeedRandomly = false;
-      RunAlgorithm(vns);
-      Assert.AreEqual(867, GetDoubleResult(vns, "BestQuality"));
-      Assert.AreEqual(867, GetDoubleResult(vns, "CurrentAverageQuality"));
-      Assert.AreEqual(867, GetDoubleResult(vns, "CurrentWorstQuality"));
-      Assert.AreEqual(12975173, GetIntResult(vns, "EvaluatedSolutions"));
-    }
-
-    private VariableNeighborhoodSearch CreateVnsTspSample() {
-      VariableNeighborhoodSearch vns = new VariableNeighborhoodSearch();
-      #region Problem Configuration
-      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
-      tspProblem.BestKnownSolution = new Permutation(PermutationTypes.Absolute, new int[] {
-117, 65, 73, 74, 75, 76, 82, 86, 87, 94, 100, 106, 115, 120, 124, 107, 101, 108, 109, 102, 97, 90, 96, 95, 88, 89, 84, 78, 69, 57, 68, 56, 44, 55, 45, 36, 46, 37, 38, 47, 48, 59, 49, 58, 70, 77, 83, 79, 50, 80, 85, 98, 103, 110, 116, 121, 125, 133, 132, 138, 139, 146, 147, 159, 168, 169, 175, 182, 188, 201, 213, 189, 214, 221, 230, 246, 262, 276, 284, 275, 274, 261, 245, 229, 220, 228, 243, 259, 273, 282, 272, 258, 242, 257, 293, 292, 302, 310, 319, 320, 327, 326, 333, 340, 346, 339, 345, 344, 337, 338, 332, 325, 318, 309, 301, 291, 271, 251, 270, 233, 250, 269, 268, 280, 290, 300, 415, 440, 416, 417, 441, 458, 479, 418, 419, 395, 420, 442, 421, 396, 397, 422, 423, 461, 481, 502, 460, 501, 459, 480, 500, 517, 531, 516, 530, 499, 478, 457, 439, 414, 413, 412, 438, 456, 477, 498, 515, 529, 538, 547, 558, 559, 560, 548, 539, 549, 561, 562, 551, 550, 532, 540, 533, 541, 518, 534, 542, 552, 553, 554, 555, 535, 543, 556, 544, 536, 522, 505, 521, 520, 504, 519, 503, 482, 462, 463, 464, 483, 443, 465, 484, 506, 485, 507, 508, 487, 467, 486, 466, 445, 428, 444, 424, 425, 426, 427, 398, 399, 400, 381, 382, 371, 372, 401, 429, 446, 430, 402, 383, 366, 356, 357, 352, 385, 384, 403, 431, 447, 469, 468, 488, 489, 490, 470, 471, 448, 432, 433, 404, 405, 386, 373, 374, 367, 376, 375, 387, 491, 509, 537, 510, 492, 472, 449, 388, 389, 406, 450, 407, 377, 368, 359, 354, 350, 335, 324, 330, 390, 434, 451, 473, 493, 511, 523, 545, 563, 565, 567, 570, 569, 578, 577, 576, 575, 574, 573, 572, 580, 584, 583, 582, 587, 586, 585, 581, 579, 571, 568, 566, 564, 557, 546, 527, 513, 526, 525, 524, 512, 495, 494, 474, 452, 436, 409, 435, 453, 475, 496, 514, 528, 497, 455, 476, 454, 437, 411, 410, 394, 393, 392, 380, 370, 379, 408, 391, 378, 369, 364, 365, 361, 355, 351, 343, 336, 331, 317, 299, 286, 287, 278, 263, 264, 265, 223, 202, 248, 266, 279, 288, 289, 281, 267, 249, 232, 224, 216, 215, 204, 192, 193, 194, 186, 179, 185, 203, 191, 190, 177, 171, 161, 128, 135, 140, 149, 162, 150, 163, 172, 178, 173, 164, 152, 151, 141, 153, 165, 154, 142, 155, 143, 137, 136, 130, 129, 118, 114, 113, 105, 119, 123, 131, 144, 156, 157, 145, 158, 166, 167, 174, 180, 181, 187, 195, 205, 217, 226, 236, 225, 234, 252, 235, 253, 254, 255, 238, 239, 240, 241, 256, 237, 206, 207, 208, 196, 197, 198, 209, 199, 200, 211, 212, 219, 210, 218, 227, 244, 260, 283, 294, 295, 303, 296, 311, 304, 297, 298, 305, 285, 306, 314, 329, 321, 313, 312, 328, 334, 341, 347, 348, 353, 358, 362, 363, 360, 349, 342, 322, 323, 315, 316, 308, 307, 277, 247, 231, 222, 184, 183, 176, 170, 160, 148, 134, 127, 126, 111, 104, 92, 91, 71, 60, 51, 52, 40, 32, 23, 21, 20, 18, 17, 16, 14, 13, 11, 10, 7, 6, 5, 2, 1, 0, 3, 4, 31, 39, 25, 30, 35, 34, 33, 43, 54, 42, 27, 28, 29, 9, 8, 12, 15, 19, 22, 24, 26, 41, 67, 66, 64, 63, 53, 62, 61, 72, 81, 93, 99, 112, 122, 
-      });
-      tspProblem.Coordinates = new DoubleMatrix(new double[,] {
-{48, 71}, {49, 71}, {50, 71}, {44, 70}, {45, 70}, {52, 70}, {53, 70}, {54, 70}, {41, 69}, {42, 69}, {55, 69}, {56, 69}, {40, 68}, {56, 68}, {57, 68}, {39, 67}, {57, 67}, {58, 67}, {59, 67}, {38, 66}, {59, 66}, {60, 66}, {37, 65}, {60, 65}, {36, 64}, {43, 64}, {35, 63}, {37, 63}, {41, 63}, {42, 63}, {43, 63}, {47, 63}, {61, 63}, {40, 62}, {41, 62}, {42, 62}, {43, 62}, {45, 62}, {46, 62}, {47, 62}, {62, 62}, {34, 61}, {38, 61}, {39, 61}, {42, 61}, {43, 61}, {44, 61}, {45, 61}, {46, 61}, {47, 61}, {52, 61}, {62, 61}, {63, 61}, {26, 60}, {38, 60}, {42, 60}, {43, 60}, {44, 60}, {46, 60}, {47, 60}, {63, 60}, {23, 59}, {24, 59}, {27, 59}, {29, 59}, {30, 59}, {31, 59}, {33, 59}, {42, 59}, {46, 59}, {47, 59}, {63, 59}, {21, 58}, {32, 58}, {33, 58}, {34, 58}, {35, 58}, {46, 58}, {47, 58}, {48, 58}, {53, 58}, {21, 57}, {35, 57}, {47, 57}, {48, 57}, {53, 57}, {36, 56}, {37, 56}, {46, 56}, {47, 56}, {48, 56}, {64, 56}, {65, 56}, {20, 55}, {38, 55}, {46, 55}, {47, 55}, {48, 55}, {52, 55}, {21, 54}, {40, 54}, {47, 54}, {48, 54}, {52, 54}, {65, 54}, {30, 53}, {41, 53}, {46, 53}, {47, 53}, {48, 53}, {52, 53}, {65, 53}, {21, 52}, {32, 52}, {33, 52}, {42, 52}, {51, 52}, {21, 51}, {33, 51}, {34, 51}, {43, 51}, {51, 51}, {21, 50}, {35, 50}, {44, 50}, {50, 50}, {66, 50}, {67, 50}, {21, 49}, {34, 49}, {36, 49}, {37, 49}, {46, 49}, {49, 49}, {67, 49}, {22, 48}, {36, 48}, {37, 48}, {46, 48}, {47, 48}, {22, 47}, {30, 47}, {34, 47}, {37, 47}, {38, 47}, {39, 47}, {47, 47}, {48, 47}, {67, 47}, {23, 46}, {28, 46}, {29, 46}, {30, 46}, {31, 46}, {32, 46}, {35, 46}, {37, 46}, {38, 46}, {39, 46}, {49, 46}, {67, 46}, {23, 45}, {28, 45}, {29, 45}, {31, 45}, {32, 45}, {40, 45}, {41, 45}, {49, 45}, {50, 45}, {68, 45}, {24, 44}, {29, 44}, {32, 44}, {41, 44}, {51, 44}, {68, 44}, {25, 43}, {30, 43}, {32, 43}, {42, 43}, {43, 43}, {51, 43}, {68, 43}, {69, 43}, {31, 42}, {32, 42}, {43, 42}, {52, 42}, {55, 42}, {26, 41}, {27, 41}, {31, 41}, {32, 41}, {33, 41}, {44, 41}, {45, 41}, {46, 41}, {47, 41}, {48, 41}, {49, 41}, {53, 41}, {25, 40}, {27, 40}, {32, 40}, {43, 40}, {44, 40}, {45, 40}, {46, 40}, {48, 40}, {49, 40}, {50, 40}, {51, 40}, {53, 40}, {56, 40}, {32, 39}, {33, 39}, {43, 39}, {50, 39}, {51, 39}, {54, 39}, {56, 39}, {69, 39}, {24, 38}, {32, 38}, {41, 38}, {42, 38}, {51, 38}, {52, 38}, {54, 38}, {57, 38}, {69, 38}, {31, 37}, {32, 37}, {40, 37}, {41, 37}, {42, 37}, {43, 37}, {44, 37}, {45, 37}, {46, 37}, {47, 37}, {48, 37}, {51, 37}, {52, 37}, {55, 37}, {57, 37}, {69, 37}, {24, 36}, {31, 36}, {32, 36}, {39, 36}, {40, 36}, {41, 36}, {42, 36}, {43, 36}, {45, 36}, {48, 36}, {49, 36}, {51, 36}, {53, 36}, {55, 36}, {58, 36}, {22, 35}, {23, 35}, {24, 35}, {25, 35}, {30, 35}, {31, 35}, {32, 35}, {39, 35}, {41, 35}, {49, 35}, {51, 35}, {55, 35}, {56, 35}, {58, 35}, {71, 35}, {20, 34}, {27, 34}, {30, 34}, {31, 34}, {51, 34}, {53, 34}, {57, 34}, {60, 34}, {18, 33}, {19, 33}, {29, 33}, {30, 33}, {31, 33}, {45, 33}, {46, 33}, {47, 33}, {52, 33}, {53, 33}, {55, 33}, {57, 33}, {58, 33}, {17, 32}, {30, 32}, {44, 32}, {47, 32}, {54, 32}, {57, 32}, {59, 32}, {61, 32}, {71, 32}, {72, 32}, {43, 31}, {47, 31}, {56, 31}, {58, 31}, {59, 31}, {61, 31}, {72, 31}, {74, 31}, {16, 30}, {43, 30}, {46, 30}, {47, 30}, {59, 30}, {63, 30}, {71, 30}, {75, 30}, {43, 29}, {46, 29}, {47, 29}, {59, 29}, {60, 29}, {75, 29}, {15, 28}, {43, 28}, {46, 28}, {61, 28}, {76, 28}, {15, 27}, {43, 27}, {44, 27}, {45, 27}, {46, 27}, {60, 27}, {62, 27}, {15, 26}, {43, 26}, {44, 26}, {46, 26}, {59, 26}, {60, 26}, {64, 26}, {77, 26}, {15, 25}, {58, 25}, {61, 25}, {77, 25}, {15, 24}, {53, 24}, {55, 24}, {61, 24}, {77, 24}, {62, 23}, {16, 22}, {61, 22}, {62, 22}, {15, 21}, {16, 21}, {52, 21}, {63, 21}, {77, 21}, {16, 20}, {17, 20}, {46, 20}, {47, 20}, {60, 20}, {62, 20}, {63, 20}, {65, 20}, {76, 20}, {15, 19}, {17, 19}, {18, 19}, {44, 19}, {45, 19}, {48, 19}, {53, 19}, {56, 19}, {60, 19}, {62, 19}, {67, 19}, {68, 19}, {76, 19}, {15, 18}, {18, 18}, {19, 18}, {20, 18}, {32, 18}, {33, 18}, {34, 18}, {41, 18}, {42, 18}, {43, 18}, {46, 18}, {48, 18}, {53, 18}, {59, 18}, {60, 18}, {69, 18}, {75, 18}, {16, 17}, {17, 17}, {20, 17}, {21, 17}, {22, 17}, {23, 17}, {24, 17}, {26, 17}, {28, 17}, {29, 17}, {30, 17}, {31, 17}, {32, 17}, {34, 17}, {35, 17}, {36, 17}, {37, 17}, {38, 17}, {39, 17}, {40, 17}, {44, 17}, {46, 17}, {48, 17}, {53, 17}, {56, 17}, {58, 17}, {75, 17}, {17, 16}, {18, 16}, {20, 16}, {24, 16}, {26, 16}, {27, 16}, {29, 16}, {33, 16}, {41, 16}, {42, 16}, {44, 16}, {47, 16}, {52, 16}, {57, 16}, {70, 16}, {73, 16}, {74, 16}, {17, 15}, {18, 15}, {20, 15}, {22, 15}, {24, 15}, {27, 15}, {29, 15}, {31, 15}, {33, 15}, {35, 15}, {36, 15}, {38, 15}, {39, 15}, {42, 15}, {45, 15}, {47, 15}, {52, 15}, {53, 15}, {55, 15}, {56, 15}, {70, 15}, {73, 15}, {17, 14}, {19, 14}, {21, 14}, {24, 14}, {26, 14}, {29, 14}, {31, 14}, {34, 14}, {37, 14}, {40, 14}, {42, 14}, {44, 14}, {46, 14}, {47, 14}, {53, 14}, {54, 14}, {55, 14}, {62, 14}, {70, 14}, {72, 14}, {17, 13}, {19, 13}, {21, 13}, {23, 13}, {25, 13}, {27, 13}, {30, 13}, {32, 13}, {34, 13}, {36, 13}, {38, 13}, {41, 13}, {43, 13}, {44, 13}, {45, 13}, {60, 13}, {70, 13}, {71, 13}, {18, 12}, {21, 12}, {23, 12}, {26, 12}, {28, 12}, {31, 12}, {34, 12}, {37, 12}, {39, 12}, {41, 12}, {42, 12}, {70, 12}, {18, 11}, {19, 11}, {20, 11}, {21, 11}, {24, 11}, {25, 11}, {27, 11}, {29, 11}, {31, 11}, {33, 11}, {35, 11}, {38, 11}, {41, 11}, {59, 11}, {26, 10}, {29, 10}, {32, 10}, {34, 10}, {36, 10}, {39, 10}, {40, 10}, {69, 10}, {21, 9}, {26, 9}, {28, 9}, {30, 9}, {32, 9}, {33, 9}, {35, 9}, {36, 9}, {37, 9}, {38, 9}, {39, 9}, {22, 8}, {27, 8}, {28, 8}, {29, 8}, {30, 8}, {31, 8}, {68, 8}, {23, 7}, {66, 7}, {24, 6}, {65, 6}, {25, 5}, {62, 5}, {63, 5}, {26, 4}, {55, 4}, {56, 4}, {57, 4}, {58, 4}, {59, 4}, {60, 4}, {61, 4}, {28, 3}, {53, 3}, {29, 2}, {50, 2}, {51, 2}, {52, 2}, {31, 1}, {32, 1}, {48, 1}
-      });
-      tspProblem.BestKnownQuality = new DoubleValue(867);
-
-      tspProblem.EvaluatorParameter.Value = new TSPRoundedEuclideanPathEvaluator();
-      tspProblem.SolutionCreatorParameter.Value = new RandomPermutationCreator();
-      tspProblem.UseDistanceMatrix.Value = true;
-      tspProblem.Name = "Funny TSP";
-      tspProblem.Description = "Represents a symmetric Traveling Salesman Problem.";
-      #endregion
-      #region Algorithm Configuration
-      vns.Name = "Variable Neighborhood Search - TSP";
-      vns.Description = "A variable neighborhood search algorithm which solves a funny TSP instance";
-      vns.Problem = tspProblem;
-
-      var localImprovement = vns.LocalImprovementParameter.ValidValues
-        .OfType<LocalSearchImprovementOperator>()
-        .Single();
-      // move generator has to be set first
-      localImprovement.MoveGenerator = localImprovement.MoveGeneratorParameter.ValidValues
-        .OfType<StochasticInversionMultiMoveGenerator>()
-        .Single();
-      localImprovement.MoveEvaluator = localImprovement.MoveEvaluatorParameter.ValidValues
-        .OfType<TSPInversionMoveRoundedEuclideanPathEvaluator>()
-        .Single();
-      localImprovement.MoveMaker = localImprovement.MoveMakerParameter.ValidValues
-        .OfType<InversionMoveMaker>()
-        .Single();
-      localImprovement.SampleSizeParameter.Value = new IntValue(500);
-      vns.LocalImprovement = localImprovement;
-
-      vns.LocalImprovementMaximumIterations = 150;
-      vns.MaximumIterations = 25;
-      vns.Seed = 0;
-      vns.SetSeedRandomly = true;
-      var shakingOperator = vns.ShakingOperatorParameter.ValidValues
-        .OfType<PermutationShakingOperator>()
-        .Single();
-      shakingOperator.Operators.SetItemCheckedState(shakingOperator.Operators
-        .OfType<Swap2Manipulator>()
-        .Single(), false);
-      shakingOperator.Operators.SetItemCheckedState(shakingOperator.Operators
-        .OfType<Swap3Manipulator>()
-        .Single(), false);
-      vns.ShakingOperator = shakingOperator;
-      #endregion
-      vns.Engine = new ParallelEngine.ParallelEngine();
-      return vns;
-    }
-    #endregion
-    #endregion
-
-    #region Gaussian Process Regression
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateGaussianProcessRegressionSampleTest() {
-      var gpr = CreateGaussianProcessRegressionSample();
-      XmlGenerator.Serialize(gpr, @"Samples\GPR.hl");
-    }
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunGaussianProcessRegressionSample() {
-      var gpr = CreateGaussianProcessRegressionSample();
-      gpr.SetSeedRandomly = false;
-      gpr.Seed = 1618551877;
-      RunAlgorithm(gpr);
-      Assert.AreEqual(-940.48768748097029, GetDoubleResult(gpr, "NegativeLogLikelihood"));
-      Assert.AreEqual(0.99561947047986976, GetDoubleResult(gpr, "Training R²"));
-    }
-
-    private GaussianProcessRegression CreateGaussianProcessRegressionSample() {
-      var gpr = new GaussianProcessRegression();
-      var provider = new VariousInstanceProvider();
-      var instance = provider.GetDataDescriptors().Where(x => x.Name.Contains("Spatial co-evolution")).Single();
-      var regProblem = new RegressionProblem();
-      regProblem.Load(provider.LoadData(instance));
-      #region Algorithm Configuration
-      gpr.Name = "Gaussian Process Regression";
-      gpr.Description = "A Gaussian process regression algorithm which solves the spatial co-evolution benchmark problem";
-      gpr.Problem = regProblem;
-
-      gpr.CovarianceFunction = new CovarianceSquaredExponentialIso();
-      gpr.MeanFunction = new MeanConst();
-      gpr.MinimizationIterations = 20;
-      gpr.Seed = 0;
-      gpr.SetSeedRandomly = true;
-      #endregion
-      gpr.Engine = new ParallelEngine.ParallelEngine();
-      return gpr;
-    }
-    #endregion
-
-    #region Scatter Search
-    #region VRP
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateScatterSearchVRPSampleTest() {
-      var ss = CreateScatterSearchVRPSample();
-      XmlGenerator.Serialize(ss, @"Samples\SS_VRP.hl");
-    }
-
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunScatterSearchVRPSampleTest() {
-      var ss = CreateScatterSearchVRPSample();
-      ss.SetSeedRandomly.Value = false;
-      RunAlgorithm(ss);
-      Assert.AreEqual(828.93686694283383, GetDoubleResult(ss, "BestQuality"));
-      Assert.AreEqual(868.63623986983077, GetDoubleResult(ss, "CurrentAverageQuality"));
-      Assert.AreEqual(1048.8333559209832, GetDoubleResult(ss, "CurrentWorstQuality"));
-      Assert.AreEqual(262622, GetIntResult(ss, "EvaluatedSolutions"));
-    }
-
-    private ScatterSearch CreateScatterSearchVRPSample() {
-      #region Problem Configuration
-      var provider = new SolomonInstanceProvider();
-      var instance = provider.GetDataDescriptors().Single(x => x.Name == "C101");
-      VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
-      vrpProblem.Load(provider.LoadData(instance));
-      #endregion
-
-      #region Algorithm Configuration
-      ScatterSearch ss = new ScatterSearch();
-      ss.Engine = new SequentialEngine.SequentialEngine();
-      ss.Name = "Scatter Search - VRP";
-      ss.Description = "A scatter search algorithm which solves the \"C101\" vehicle routing problem (imported from Solomon)";
-      ss.Problem = vrpProblem;
-
-      var improver = ss.Problem.Operators.OfType<VRPIntraRouteImprovementOperator>().First();
-      improver.ImprovementAttemptsParameter.Value.Value = 15;
-      improver.SampleSizeParameter.Value.Value = 10;
-      ss.Improver = improver;
-
-      var pathRelinker = ss.Problem.Operators.OfType<VRPPathRelinker>().First();
-      pathRelinker.IterationsParameter.Value.Value = 25;
-      ss.PathRelinker = pathRelinker;
-
-      var similarityCalculator = ss.SimilarityCalculatorParameter.ValidValues.OfType<VRPSimilarityCalculator>().First();
-      ss.SimilarityCalculator = similarityCalculator;
-
-      ss.MaximumIterations.Value = 2;
-      ss.PopulationSize.Value = 20;
-      ss.ReferenceSetSize.Value = 10;
-      ss.Seed.Value = 0;
-      return ss;
-      #endregion
-    }
-    #endregion
-    #endregion
-
-    #region RAPGA
-    #region Scheduling
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateRAPGASchedulingSampleTest() {
-      var ss = CreateRAPGASchedulingSample();
-      XmlGenerator.Serialize(ss, @"Samples\RAPGA_JSSP.hl");
-    }
-
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunRAPGASchedulingSampleTest() {
-      var rapga = CreateRAPGASchedulingSample();
-      rapga.SetSeedRandomly.Value = false;
-      RunAlgorithm(rapga);
-      Assert.AreEqual(988.00, GetDoubleResult(rapga, "BestQuality"));
-      Assert.AreEqual(988.00, GetDoubleResult(rapga, "CurrentAverageQuality"));
-      Assert.AreEqual(988.00, GetDoubleResult(rapga, "CurrentWorstQuality"));
-      Assert.AreEqual(27100, GetIntResult(rapga, "EvaluatedSolutions"));
-    }
-
-    private RAPGA CreateRAPGASchedulingSample() {
-      #region Problem Configuration
-      JobShopSchedulingProblem problem = new JobShopSchedulingProblem();
-      #endregion
-
-      #region Algorithm Configuration
-      RAPGA rapga = new RAPGA();
-      rapga.Engine = new SequentialEngine.SequentialEngine();
-      rapga.Name = "RAPGA - Job Shop Scheduling";
-      rapga.Description = "A relevant alleles preserving genetic algorithm which solves a job shop scheduling problem";
-      rapga.Problem = problem;
-      rapga.Mutator = rapga.MutatorParameter.ValidValues.OfType<JSMSwapManipulator>().First();
-      rapga.Seed.Value = 0;
-      return rapga;
-      #endregion
-    }
-    #endregion
-    #endregion
-
-    #region grammatical evolution
-    #region artificial ant
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateGeArtificialAntSampleTest() {
-      var geaa = CreateGeArtificialAntSample();
-      XmlGenerator.Serialize(geaa, @"Samples\GE_ArtificialAnt.hl");
-    }
-
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunGeArtificalAntSampleTest() {
-      var ga = CreateGeArtificialAntSample();
-      ga.SetSeedRandomly.Value = false;
-      RunAlgorithm(ga);
-    }
-
-    public OffspringSelectionGeneticAlgorithm CreateGeArtificialAntSample() {
-      OffspringSelectionGeneticAlgorithm ga = new OffspringSelectionGeneticAlgorithm();
-      #region Problem Configuration
-      var problem = new HeuristicLab.Problems.GrammaticalEvolution.GEArtificialAntProblem();
-      #endregion
-      #region Algorithm Configuration
-      ga.Name = "Grammatical Evolution - Artificial Ant (SantaFe)";
-      ga.Description = "Grammatical evolution algorithm for solving a artificial ant problem";
-      ga.Problem = problem;
-      ConfigureOsGeneticAlgorithmParameters<GenderSpecificSelector, Encodings.IntegerVectorEncoding.SinglePointCrossover, Encodings.IntegerVectorEncoding.UniformOnePositionManipulator>(
-        ga, 200, 1, 50, 0.05, 200);
-      #endregion
-      return ga;
-    }
-    #endregion
-
-    #region symbolic regression
-    #endregion
-    [TestMethod]
-    [TestCategory("Samples.Create")]
-    [TestProperty("Time", "medium")]
-    public void CreateGeSymbolicRegressionSampleTest() {
-      var geSymbReg = CreateGeSymbolicRegressionSample();
-      XmlGenerator.Serialize(geSymbReg, @"Samples\GE_SymbReg.hl");
-    }
-
-    [TestMethod]
-    [TestCategory("Samples.Execute")]
-    [TestProperty("Time", "long")]
-    public void RunGeSymbolicRegressionSampleTest() {
-      var ga = CreateGeSymbolicRegressionSample();
-      ga.SetSeedRandomly.Value = false;
-      RunAlgorithm(ga);
-    }
-
-    public OffspringSelectionGeneticAlgorithm CreateGeSymbolicRegressionSample() {
-      var ga = new OffspringSelectionGeneticAlgorithm();
-      #region Problem Configuration
-      var problem = new HeuristicLab.Problems.GrammaticalEvolution.GESymbolicRegressionSingleObjectiveProblem();
-
-      #endregion
-      #region Algorithm Configuration
-      ga.Name = "Grammatical Evolution - Symbolic Regression (Poly-10)";
-      ga.Description = "Grammatical evolution algorithm for solving a symbolic regression problem problem";
-      ga.Problem = problem;
-      problem.Load(new PolyTen().GenerateRegressionData());
-
-      // must occur after loading problem data because the grammar creates symbols for random constants once the data is loaded
-      var consts = problem.SymbolicExpressionTreeGrammar.AllowedSymbols.OfType<Constant>().ToList();
-      foreach (var c in consts) {
-        problem.SymbolicExpressionTreeGrammar.RemoveSymbol(c);
-      }
-
-      ConfigureOsGeneticAlgorithmParameters<GenderSpecificSelector, Encodings.IntegerVectorEncoding.SinglePointCrossover, Encodings.IntegerVectorEncoding.UniformOnePositionManipulator>(
-        ga, 1000, 1, 50, 0.05, 200);
-      #endregion
-      return ga;
-    }
-    #endregion
-
-    #region Helpers
-    private void ConfigureEvolutionStrategyParameters<R, M, SC, SR, SM>(EvolutionStrategy es, int popSize, int children, int parentsPerChild, int maxGens, bool plusSelection)
-      where R : ICrossover
-      where M : IManipulator
-      where SC : IStrategyParameterCreator
-      where SR : IStrategyParameterCrossover
-      where SM : IStrategyParameterManipulator {
-      es.PopulationSize.Value = popSize;
-      es.Children.Value = children;
-      es.ParentsPerChild.Value = parentsPerChild;
-      es.MaximumGenerations.Value = maxGens;
-      es.PlusSelection.Value = false;
-
-      es.Seed.Value = 0;
-      es.SetSeedRandomly.Value = true;
-
-      es.Recombinator = es.RecombinatorParameter.ValidValues
-        .OfType<R>()
-        .Single();
-
-      es.Mutator = es.MutatorParameter.ValidValues
-        .OfType<M>()
-        .Single();
-
-      es.StrategyParameterCreator = es.StrategyParameterCreatorParameter.ValidValues
-        .OfType<SC>()
-        .Single();
-      es.StrategyParameterCrossover = es.StrategyParameterCrossoverParameter.ValidValues
-        .OfType<SR>()
-        .Single();
-      es.StrategyParameterManipulator = es.StrategyParameterManipulatorParameter.ValidValues
-        .OfType<SM>()
-        .Single();
-      es.Engine = new ParallelEngine.ParallelEngine();
-    }
-
-    private void ConfigureGeneticAlgorithmParameters<S, C, M>(GeneticAlgorithm ga, int popSize, int elites, int maxGens, double mutationRate, int tournGroupSize = 0)
-      where S : ISelector
-      where C : ICrossover
-      where M : IManipulator {
-      ga.Elites.Value = elites;
-      ga.MaximumGenerations.Value = maxGens;
-      ga.MutationProbability.Value = mutationRate;
-      ga.PopulationSize.Value = popSize;
-      ga.Seed.Value = 0;
-      ga.SetSeedRandomly.Value = true;
-      ga.Selector = ga.SelectorParameter.ValidValues
-        .OfType<S>()
-        .First();
-
-      ga.Crossover = ga.CrossoverParameter.ValidValues
-        .OfType<C>()
-        .First();
-
-      ga.Mutator = ga.MutatorParameter.ValidValues
-        .OfType<M>()
-        .First();
-
-      var tSelector = ga.Selector as TournamentSelector;
-      if (tSelector != null) {
-        tSelector.GroupSizeParameter.Value.Value = tournGroupSize;
-      }
-      ga.Engine = new ParallelEngine.ParallelEngine();
-    }
-
-    private void ConfigureOsGeneticAlgorithmParameters<S, C, M>(OffspringSelectionGeneticAlgorithm ga, int popSize, int elites, int maxGens, double mutationRate=0.05, double maxSelPres=100, int tournGroupSize = 0)
-      where S : ISelector
-      where C : ICrossover
-      where M : IManipulator {
-      ga.Elites.Value = elites;
-      ga.MaximumGenerations.Value = maxGens;
-      ga.MutationProbability.Value = mutationRate;
-      ga.PopulationSize.Value = popSize;
-      ga.MaximumSelectionPressure.Value = maxSelPres;
-      ga.Seed.Value = 0;
-      ga.SetSeedRandomly.Value = true;
-      ga.ComparisonFactorLowerBound.Value = 1;
-      ga.ComparisonFactorUpperBound.Value = 1;
-
-      ga.Selector = ga.SelectorParameter.ValidValues
-        .OfType<S>()
-        .First();
-
-      ga.Crossover = ga.CrossoverParameter.ValidValues
-        .OfType<C>()
-        .First();
-
-      ga.Mutator = ga.MutatorParameter.ValidValues
-        .OfType<M>()
-        .First();
-
-      var tSelector = ga.Selector as TournamentSelector;
-      if (tSelector != null) {
-        tSelector.GroupSizeParameter.Value.Value = tournGroupSize;
-      }
-      ga.Engine = new ParallelEngine.ParallelEngine();
-    }
-
-    private void ConfigureIslandGeneticAlgorithmParameters<S, C, M, Mi, MiS, MiR>(IslandGeneticAlgorithm ga, int popSize, int elites, int maxGens, double mutationRate, int numberOfIslands, int migrationInterval, double migrationRate)
-      where S : ISelector
-      where C : ICrossover
-      where M : IManipulator
-      where Mi : IMigrator
-      where MiS : ISelector
-      where MiR : IReplacer {
-      ga.Elites.Value = elites;
-      ga.MaximumGenerations.Value = maxGens;
-      ga.MutationProbability.Value = mutationRate;
-      ga.PopulationSize.Value = popSize;
-      ga.NumberOfIslands.Value = numberOfIslands;
-      ga.MigrationInterval.Value = migrationInterval;
-      ga.MigrationRate.Value = migrationRate;
-      ga.Seed.Value = 0;
-      ga.SetSeedRandomly.Value = true;
-      ga.Selector = ga.SelectorParameter.ValidValues
-        .OfType<S>()
-        .Single();
-
-      ga.Crossover = ga.CrossoverParameter.ValidValues
-        .OfType<C>()
-        .Single();
-
-      ga.Mutator = ga.MutatorParameter.ValidValues
-        .OfType<M>()
-        .Single();
-      ga.Migrator = ga.MigratorParameter.ValidValues
-        .OfType<Mi>()
-        .Single();
-      ga.EmigrantsSelector = ga.EmigrantsSelectorParameter.ValidValues
-        .OfType<MiS>()
-        .Single();
-      ga.ImmigrationReplacer = ga.ImmigrationReplacerParameter.ValidValues
-        .OfType<MiR>()
-        .Single();
-      ga.Engine = new ParallelEngine.ParallelEngine();
-    }
-
-
-    private void RunAlgorithm(IAlgorithm a) {
-      var trigger = new EventWaitHandle(false, EventResetMode.ManualReset);
-      Exception ex = null;
-      a.Stopped += (src, e) => { trigger.Set(); };
-      a.ExceptionOccurred += (src, e) => { ex = e.Value; trigger.Set(); };
-      a.Prepare();
-      a.Start();
-      trigger.WaitOne();
-
-      Assert.AreEqual(ex, null);
-    }
-
-    private double GetDoubleResult(IAlgorithm a, string resultName) {
-      return ((DoubleValue)a.Results[resultName].Value).Value;
-    }
-    private int GetIntResult(IAlgorithm a, string resultName) {
-      return ((IntValue)a.Results[resultName].Value).Value;
-    }
-    #endregion
-  }
-}
Index: trunk/sources/HeuristicLab.Tests/HeuristicLab.Tests.csproj
===================================================================
--- trunk/sources/HeuristicLab.Tests/HeuristicLab.Tests.csproj	(revision 11050)
+++ trunk/sources/HeuristicLab.Tests/HeuristicLab.Tests.csproj	(revision 11051)
@@ -357,5 +357,7 @@
     <Compile Include="HeuristicLab-3.3\PluginDependenciesTest.cs" />
     <Compile Include="HeuristicLab-3.3\PluginLoader.cs" />
-    <Compile Include="HeuristicLab-3.3\SamplesTest.cs" />
+    <Compile Include="HeuristicLab-3.3\Samples\GPMultiplexerSampleTest.cs" />
+    <Compile Include="HeuristicLab-3.3\Samples\SamplesTest.cs" />
+    <Compile Include="HeuristicLab-3.3\Samples\SamplesUtils.cs" />
     <Compile Include="HeuristicLab-3.3\StorableConstructorTest.cs" />
     <Compile Include="HeuristicLab-3.3\ThreadSafeLogTest.cs" />
@@ -489,4 +491,7 @@
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
+    <None Include="Test Resources\Multiplexer11.csv">
+      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
+    </None>
     <None Include="Test Resources\SamplesExperimentFinished.hl">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
Index: trunk/sources/HeuristicLab.Tests/Test Resources/multiplexer11.csv
===================================================================
--- trunk/sources/HeuristicLab.Tests/Test Resources/multiplexer11.csv	(revision 11051)
+++ trunk/sources/HeuristicLab.Tests/Test Resources/multiplexer11.csv	(revision 11051)
@@ -0,0 +1,2049 @@
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