sources/HeuristicLab/3.3/Tests/SamplesTest.cs

   1 using System;
   2 using System.Text;
   3 using System.Collections.Generic;
   4 using System.Linq;
   5 using Microsoft.VisualStudio.TestTools.UnitTesting;
   6 using HeuristicLab.Algorithms.GeneticAlgorithm;
   7 using HeuristicLab.Problems.ArtificialAnt;
   8 using HeuristicLab.Selection;
   9 using HeuristicLab.Data;
  10 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
  11 using HeuristicLab.Persistence.Default.Xml;
  12 using HeuristicLab.Optimization;
  13 using System.Threading;
  14 using HeuristicLab.ParallelEngine;
  15 using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
  16 using HeuristicLab.Problems.DataAnalysis;
  17 using HeuristicLab.Problems.DataAnalysis.Symbolic;
  18 using System.IO;
  19 using HeuristicLab.Problems.DataAnalysis.Symbolic.Classification;
  20 using HeuristicLab.Problems.TravelingSalesman;
  21 using HeuristicLab.Encodings.PermutationEncoding;
  22 using HeuristicLab.Problems.VehicleRouting;
  23 using HeuristicLab.Problems.VehicleRouting.Encodings.Potvin;
  24 using HeuristicLab.Problems.VehicleRouting.Encodings;
  25 using HeuristicLab.Problems.VehicleRouting.Encodings.General;
  26 using HeuristicLab.Algorithms.EvolutionStrategy;
  27 using HeuristicLab.Encodings.RealVectorEncoding;
  28 using HeuristicLab.Problems.TestFunctions;
  29 using HeuristicLab.Optimization.Operators;
  30 using HeuristicLab.Algorithms.LocalSearch;
  31 using HeuristicLab.Problems.Knapsack;
  32 using HeuristicLab.Encodings.BinaryVectorEncoding;
  33 using HeuristicLab.Algorithms.ParticleSwarmOptimization;
  34 using HeuristicLab.Algorithms.SimulatedAnnealing;
  35 using HeuristicLab.Algorithms.TabuSearch;
  36 using HeuristicLab.Algorithms.VariableNeighborhoodSearch;
  37
  38 namespace HeuristicLab_33.Tests {
  39   [TestClass]
  40   [DeploymentItem(@"Resources/C101.opt.txt")]
  41   [DeploymentItem(@"Resources/C101.txt")]
  42   [DeploymentItem(@"Resources/ch130.tsp")]
  43   [DeploymentItem(@"Resources/ch130.opt.tour")]
  44   [DeploymentItem(@"Resources/mammographic_masses.txt")]
  45   [DeploymentItem(@"Resources/towerData.txt")]
  46   public class SamplesTest {
  47     #region GA
  48     #region TSP
  49     [TestMethod]
  50     public void CreateGaTspSampleTest() {
  51       var ga = CreateGaTspSample();
  52       XmlGenerator.Serialize(ga, "../../GA_TSP.hl");
  53     }
  54     [TestMethod]
  55     public void RunGaTspSampleTest() {
  56       var ga = CreateGaTspSample();
  57       ga.SetSeedRandomly.Value = false;
  58       RunAlgorithm(ga);
  59       Assert.AreEqual(12332, GetDoubleResult(ga, "BestQuality"));
  60       Assert.AreEqual(13123.2, GetDoubleResult(ga, "CurrentAverageQuality"));
  61       Assert.AreEqual(14538, GetDoubleResult(ga, "CurrentWorstQuality"));
  62       Assert.AreEqual(99100, GetIntResult(ga, "EvaluatedSolutions"));
  63     }
  64
  65     private GeneticAlgorithm CreateGaTspSample() {
  66       GeneticAlgorithm ga = new GeneticAlgorithm();
  67       #region problem configuration
  68       TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
  69       tspProblem.ImportFromTSPLIB("ch130.tsp", "ch130.opt.tour", 6110);
  70       tspProblem.Evaluator = new TSPRoundedEuclideanPathEvaluator();
  71       tspProblem.SolutionCreator = new RandomPermutationCreator();
  72       tspProblem.UseDistanceMatrix.Value = true;
  73       tspProblem.Name = "ch130 TSP (imported from TSPLIB)";
  74       tspProblem.Description = "130 city problem (Churritz)";
  75       #endregion
  76       #region algorithm configuration
  77       ga.Name = "Genetic Algorithm - TSP";
  78       ga.Description = "A genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
  79       ga.Problem = tspProblem;
  80       ConfigureGeneticAlgorithmParameters<ProportionalSelector, OrderCrossover2, InversionManipulator>(
  81         ga, 100, 1, 1000, 0.05);
  82
  83       ga.Analyzer.Operators.SetItemCheckedState(ga.Analyzer.Operators
  84         .OfType<TSPAlleleFrequencyAnalyzer>()
  85         .Single(), false);
  86       ga.Analyzer.Operators.SetItemCheckedState(ga.Analyzer.Operators
  87         .OfType<TSPPopulationDiversityAnalyzer>()
  88         .Single(), false);
  89       #endregion
  90       return ga;
  91     }
  92
  93     #endregion
  94     #region VRP
  95     [TestMethod]
  96     public void CreateGaVrpSampleTest() {
  97       var ga = CreateGaVrpSample();
  98       XmlGenerator.Serialize(ga, "../../GA_VRP.hl");
  99     }
 100
 101     [TestMethod]
 102     public void RunGaVrpSampleTest() {
 103       var ga = CreateGaVrpSample();
 104       ga.SetSeedRandomly.Value = false;
 105       RunAlgorithm(ga);
 106       Assert.AreEqual(1828.9368669428336, GetDoubleResult(ga, "BestQuality"));
 107       Assert.AreEqual(1829.6829520983711, GetDoubleResult(ga, "CurrentAverageQuality"));
 108       Assert.AreEqual(1867.8378222145707, GetDoubleResult(ga, "CurrentWorstQuality"));
 109       Assert.AreEqual(99100, GetIntResult(ga, "EvaluatedSolutions"));
 110     }
 111
 112     private GeneticAlgorithm CreateGaVrpSample() {
 113       GeneticAlgorithm ga = new GeneticAlgorithm();
 114       #region problem configuration
 115       VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
 116
 117       vrpProblem.ImportFromSolomon("C101.txt");
 118       vrpProblem.ImportSolution("C101.opt.txt");
 119       vrpProblem.Name = "C101 VRP (imported from Solomon)";
 120       vrpProblem.Description = "Represents a Vehicle Routing Problem.";
 121       vrpProblem.DistanceFactorParameter.Value.Value = 1;
 122       vrpProblem.FleetUsageFactorParameter.Value.Value = 100;
 123       vrpProblem.OverloadPenaltyParameter.Value.Value = 100;
 124       vrpProblem.TardinessPenaltyParameter.Value.Value = 100;
 125       vrpProblem.TimeFactorParameter.Value.Value = 0;
 126       vrpProblem.Evaluator = new VRPEvaluator();
 127       vrpProblem.MaximizationParameter.Value.Value = false;
 128       vrpProblem.SolutionCreator = new RandomCreator();
 129       vrpProblem.UseDistanceMatrix.Value = true;
 130       vrpProblem.Vehicles.Value = 25;
 131       #endregion
 132       #region algorithm configuration
 133       ga.Name = "Genetic Algorithm - VRP";
 134       ga.Description = "A genetic algorithm which solves the \"C101\" vehicle routing problem (imported from Solomon)";
 135       ga.Problem = vrpProblem;
 136       ConfigureGeneticAlgorithmParameters<TournamentSelector, MultiVRPSolutionCrossover, MultiVRPSolutionManipulator>(
 137         ga, 100, 1, 1000, 0.05, 3);
 138
 139       var xOver = (MultiVRPSolutionCrossover)ga.Crossover;
 140       foreach (var op in xOver.Operators) {
 141         xOver.Operators.SetItemCheckedState(op, false);
 142       }
 143       xOver.Operators.SetItemCheckedState(xOver.Operators
 144         .OfType<PotvinRouteBasedCrossover>()
 145         .Single(), true);
 146       xOver.Operators.SetItemCheckedState(xOver.Operators
 147         .OfType<PotvinSequenceBasedCrossover>()
 148         .Single(), true);
 149
 150       var manipulator = (MultiVRPSolutionManipulator)ga.Mutator;
 151       foreach (var op in manipulator.Operators) {
 152         manipulator.Operators.SetItemCheckedState(op, false);
 153       }
 154       manipulator.Operators.SetItemCheckedState(manipulator.Operators
 155         .OfType<PotvinOneLevelExchangeMainpulator>()
 156         .Single(), true);
 157       manipulator.Operators.SetItemCheckedState(manipulator.Operators
 158         .OfType<PotvinTwoLevelExchangeManipulator>()
 159         .Single(), true);
 160       #endregion
 161       return ga;
 162     }
 163
 164     #endregion
 165     #region ArtificialAnt
 166
 167     [TestMethod]
 168     public void CreateGpArtificialAntSampleTest() {
 169       var ga = CreateGpArtificialAntSample();
 170       XmlGenerator.Serialize(ga, "../../SGP_SantaFe.hl");
 171     }
 172
 173     [TestMethod]
 174     public void RunGpArtificialAntSampleTest() {
 175       var ga = CreateGpArtificialAntSample();
 176       ga.SetSeedRandomly.Value = false;
 177       RunAlgorithm(ga);
 178       Assert.AreEqual(89, GetDoubleResult(ga, "BestQuality"));
 179       Assert.AreEqual(68.635, GetDoubleResult(ga, "CurrentAverageQuality"));
 180       Assert.AreEqual(0, GetDoubleResult(ga, "CurrentWorstQuality"));
 181       Assert.AreEqual(50950, GetIntResult(ga, "EvaluatedSolutions"));
 182     }
 183
 184     public GeneticAlgorithm CreateGpArtificialAntSample() {
 185       GeneticAlgorithm ga = new GeneticAlgorithm();
 186       #region problem configuration
 187       ArtificialAntProblem antProblem = new ArtificialAntProblem();
 188       antProblem.BestKnownQuality.Value = 89;
 189       antProblem.MaxExpressionDepth.Value = 10;
 190       antProblem.MaxExpressionLength.Value = 100;
 191       antProblem.MaxFunctionArguments.Value = 3;
 192       antProblem.MaxFunctionDefinitions.Value = 3;
 193       antProblem.MaxTimeSteps.Value = 600;
 194       #endregion
 195       #region algorithm configuration
 196       ga.Name = "Genetic Programming - Artificial Ant";
 197       ga.Description = "A standard genetic programming algorithm to solve the artificial ant problem (Santa-Fe trail)";
 198       ga.Problem = antProblem;
 199       ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeArchitectureManipulator>(
 200         ga, 1000, 1, 50, 0.15, 5);
 201       var mutator = (MultiSymbolicExpressionTreeArchitectureManipulator)ga.Mutator;
 202       mutator.Operators.SetItemCheckedState(mutator.Operators
 203         .OfType<FullTreeShaker>()
 204         .Single(), false);
 205       mutator.Operators.SetItemCheckedState(mutator.Operators
 206         .OfType<OnePointShaker>()
 207         .Single(), false);
 208       mutator.Operators.SetItemCheckedState(mutator.Operators
 209         .OfType<ArgumentDeleter>()
 210         .Single(), false);
 211       mutator.Operators.SetItemCheckedState(mutator.Operators
 212         .OfType<SubroutineDeleter>()
 213         .Single(), false);
 214       #endregion
 215       return ga;
 216     }
 217
 218     #endregion
 219     #region symbolic regression
 220     [TestMethod]
 221     public void CreateGpSymbolicRegressionSampleTest() {
 222       var ga = CreateGpSymbolicRegressionSample();
 223       XmlGenerator.Serialize(ga, "../../SGP_SymbReg.hl");
 224     }
 225     [TestMethod]
 226     public void RunGpSymbolicRegressionSampleTest() {
 227       var ga = CreateGpSymbolicRegressionSample();
 228       ga.SetSeedRandomly.Value = false;
 229       RunAlgorithm(ga);
 230       Assert.AreEqual(0.82895806566669916, GetDoubleResult(ga, "BestQuality"));
 231       Assert.AreEqual(0.50808259256341926, GetDoubleResult(ga, "CurrentAverageQuality"));
 232       Assert.AreEqual(0, GetDoubleResult(ga, "CurrentWorstQuality"));
 233       Assert.AreEqual(50950, GetIntResult(ga, "EvaluatedSolutions"));
 234     }
 235
 236     private GeneticAlgorithm CreateGpSymbolicRegressionSample() {
 237       GeneticAlgorithm ga = new GeneticAlgorithm();
 238       #region problem configuration
 239       SymbolicRegressionSingleObjectiveProblem symbRegProblem = new SymbolicRegressionSingleObjectiveProblem();
 240       symbRegProblem.Name = "Tower Symbolic Regression Problem";
 241       symbRegProblem.Description = "Tower Dataset (downloaded from: http://vanillamodeling.com/realproblems.html)";
 242       var towerProblemData = RegressionProblemData.ImportFromFile("towerData.txt");
 243       towerProblemData.TargetVariableParameter.Value = towerProblemData.TargetVariableParameter.ValidValues
 244         .First(v => v.Value == "towerResponse");
 245       towerProblemData.InputVariables.SetItemCheckedState(
 246         towerProblemData.InputVariables.Single(x => x.Value == "x1"), true);
 247       towerProblemData.InputVariables.SetItemCheckedState(
 248         towerProblemData.InputVariables.Single(x => x.Value == "x7"), false);
 249       towerProblemData.InputVariables.SetItemCheckedState(
 250         towerProblemData.InputVariables.Single(x => x.Value == "x11"), false);
 251       towerProblemData.InputVariables.SetItemCheckedState(
 252         towerProblemData.InputVariables.Single(x => x.Value == "x16"), false);
 253       towerProblemData.InputVariables.SetItemCheckedState(
 254         towerProblemData.InputVariables.Single(x => x.Value == "x21"), false);
 255       towerProblemData.InputVariables.SetItemCheckedState(
 256         towerProblemData.InputVariables.Single(x => x.Value == "x25"), false);
 257       towerProblemData.InputVariables.SetItemCheckedState(
 258         towerProblemData.InputVariables.Single(x => x.Value == "towerResponse"), false);
 259       towerProblemData.TrainingPartition.Start = 0;
 260       towerProblemData.TrainingPartition.End = 4000;
 261       towerProblemData.TestPartition.Start = 4000;
 262       towerProblemData.TestPartition.End = 4999;
 263       towerProblemData.Name = "Data imported from towerData.txt";
 264       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";
 265       symbRegProblem.ProblemData = towerProblemData;
 266
 267       // configure grammar
 268       var grammar = new TypeCoherentExpressionGrammar();
 269       grammar.Symbols.OfType<Sine>().Single().InitialFrequency = 0.0;
 270       grammar.Symbols.OfType<Cosine>().Single().InitialFrequency = 0.0;
 271       grammar.Symbols.OfType<Tangent>().Single().InitialFrequency = 0.0;
 272       grammar.Symbols.OfType<IfThenElse>().Single().InitialFrequency = 0.0;
 273       grammar.Symbols.OfType<GreaterThan>().Single().InitialFrequency = 0.0;
 274       grammar.Symbols.OfType<LessThan>().Single().InitialFrequency = 0.0;
 275       grammar.Symbols.OfType<And>().Single().InitialFrequency = 0.0;
 276       grammar.Symbols.OfType<Or>().Single().InitialFrequency = 0.0;
 277       grammar.Symbols.OfType<Not>().Single().InitialFrequency = 0.0;
 278       grammar.Symbols.OfType<TimeLag>().Single().InitialFrequency = 0.0;
 279       grammar.Symbols.OfType<Integral>().Single().InitialFrequency = 0.0;
 280       grammar.Symbols.OfType<Derivative>().Single().InitialFrequency = 0.0;
 281       grammar.Symbols.OfType<LaggedVariable>().Single().InitialFrequency = 0.0;
 282       grammar.Symbols.OfType<VariableCondition>().Single().InitialFrequency = 0.0;
 283       var varSymbol = grammar.Symbols.OfType<Variable>().Where(x => !(x is LaggedVariable)).Single();
 284       varSymbol.WeightMu = 1.0;
 285       varSymbol.WeightSigma = 1.0;
 286       varSymbol.WeightManipulatorMu = 0.0;
 287       varSymbol.WeightManipulatorSigma = 0.05;
 288       varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
 289       var constSymbol = grammar.Symbols.OfType<Constant>().Single();
 290       constSymbol.MaxValue = 20;
 291       constSymbol.MinValue = -20;
 292       constSymbol.ManipulatorMu = 0.0;
 293       constSymbol.ManipulatorSigma = 1;
 294       constSymbol.MultiplicativeManipulatorSigma = 0.03;
 295       symbRegProblem.SymbolicExpressionTreeGrammar = grammar;
 296
 297       // configure remaining problem parameters
 298       symbRegProblem.BestKnownQuality.Value = 0.97;
 299       symbRegProblem.FitnessCalculationPartition.Start = 0;
 300       symbRegProblem.FitnessCalculationPartition.End = 2800;
 301       symbRegProblem.ValidationPartition.Start = 2800;
 302       symbRegProblem.ValidationPartition.End = 4000;
 303       symbRegProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
 304       symbRegProblem.MaximumSymbolicExpressionTreeLength.Value = 150;
 305       symbRegProblem.MaximumSymbolicExpressionTreeDepth.Value = 12;
 306       symbRegProblem.MaximumFunctionDefinitions.Value = 0;
 307       symbRegProblem.MaximumFunctionArguments.Value = 0;
 308
 309       symbRegProblem.EvaluatorParameter.Value = new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator();
 310       #endregion
 311       #region algorithm configuration
 312       ga.Problem = symbRegProblem;
 313       ga.Name = "Genetic Programming - Symbolic Regression";
 314       ga.Description = "A standard genetic programming algorithm to solve a symbolic regression problem (tower dataset)";
 315       ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
 316         ga, 1000, 1, 50, 0.15, 5);
 317       var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
 318       mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
 319       mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
 320
 321       ga.Analyzer.Operators.SetItemCheckedState(
 322         ga.Analyzer.Operators
 323         .OfType<SymbolicRegressionSingleObjectiveOverfittingAnalyzer>()
 324         .Single(), false);
 325       ga.Analyzer.Operators.SetItemCheckedState(
 326         ga.Analyzer.Operators
 327         .OfType<SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
 328         .First(), false);
 329       #endregion
 330       return ga;
 331     }
 332     #endregion
 333     #region symbolic classification
 334
 335     [TestMethod]
 336     public void CreateGpSymbolicClassificationSampleTest() {
 337       var ga = CreateGpSymbolicClassificationSample();
 338       XmlGenerator.Serialize(ga, "../../SGP_SymbClass.hl");
 339     }
 340
 341     [TestMethod]
 342     public void RunGpSymbolicClassificationSampleTest() {
 343       var ga = CreateGpSymbolicClassificationSample();
 344       ga.SetSeedRandomly.Value = false;
 345       RunAlgorithm(ga);
 346       Assert.AreEqual(0.13607488888377872, GetDoubleResult(ga, "BestQuality"));
 347       Assert.AreEqual(2.1634701155600293, GetDoubleResult(ga, "CurrentAverageQuality"));
 348       Assert.AreEqual(100.62175156249987, GetDoubleResult(ga, "CurrentWorstQuality"));
 349       Assert.AreEqual(100900, GetIntResult(ga, "EvaluatedSolutions"));
 350     }
 351
 352     private GeneticAlgorithm CreateGpSymbolicClassificationSample() {
 353       GeneticAlgorithm ga = new GeneticAlgorithm();
 354       #region problem configuration
 355       SymbolicClassificationSingleObjectiveProblem symbClassProblem = new SymbolicClassificationSingleObjectiveProblem();
 356       symbClassProblem.Name = "Mammography Classification Problem";
 357       symbClassProblem.Description = "Mammography dataset imported from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass)";
 358       var mammoData = ClassificationProblemData.ImportFromFile("mammographic_masses.txt");
 359       mammoData.TargetVariableParameter.Value = mammoData.TargetVariableParameter.ValidValues
 360         .First(v => v.Value == "Severity");
 361       mammoData.InputVariables.SetItemCheckedState(
 362         mammoData.InputVariables.Single(x => x.Value == "BI-RADS"), false);
 363       mammoData.InputVariables.SetItemCheckedState(
 364         mammoData.InputVariables.Single(x => x.Value == "Age"), true);
 365       mammoData.InputVariables.SetItemCheckedState(
 366         mammoData.InputVariables.Single(x => x.Value == "Shape"), true);
 367       mammoData.InputVariables.SetItemCheckedState(
 368         mammoData.InputVariables.Single(x => x.Value == "Margin"), true);
 369       mammoData.InputVariables.SetItemCheckedState(
 370         mammoData.InputVariables.Single(x => x.Value == "Density"), true);
 371       mammoData.InputVariables.SetItemCheckedState(
 372         mammoData.InputVariables.Single(x => x.Value == "Severity"), false);
 373       mammoData.TrainingPartition.Start = 0;
 374       mammoData.TrainingPartition.End = 800;
 375       mammoData.TestPartition.Start = 800;
 376       mammoData.TestPartition.End = 961;
 377       mammoData.Name = "Data imported from mammographic_masses.csv";
 378       mammoData.Description = "Original dataset: http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass, missing values have been replaced with median values.";
 379       symbClassProblem.ProblemData = mammoData;
 380
 381       // configure grammar
 382       var grammar = new TypeCoherentExpressionGrammar();
 383       grammar.Symbols.OfType<Sine>().Single().InitialFrequency = 0.0;
 384       grammar.Symbols.OfType<Cosine>().Single().InitialFrequency = 0.0;
 385       grammar.Symbols.OfType<Tangent>().Single().InitialFrequency = 0.0;
 386       grammar.Symbols.OfType<Power>().Single().InitialFrequency = 0.0;
 387       grammar.Symbols.OfType<Root>().Single().InitialFrequency = 0.0;
 388       grammar.Symbols.OfType<TimeLag>().Single().InitialFrequency = 0.0;
 389       grammar.Symbols.OfType<Integral>().Single().InitialFrequency = 0.0;
 390       grammar.Symbols.OfType<Derivative>().Single().InitialFrequency = 0.0;
 391       grammar.Symbols.OfType<LaggedVariable>().Single().InitialFrequency = 0.0;
 392       grammar.Symbols.OfType<VariableCondition>().Single().InitialFrequency = 0.0;
 393       var varSymbol = grammar.Symbols.OfType<Variable>().Where(x => !(x is LaggedVariable)).Single();
 394       varSymbol.WeightMu = 1.0;
 395       varSymbol.WeightSigma = 1.0;
 396       varSymbol.WeightManipulatorMu = 0.0;
 397       varSymbol.WeightManipulatorSigma = 0.05;
 398       varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
 399       var constSymbol = grammar.Symbols.OfType<Constant>().Single();
 400       constSymbol.MaxValue = 20;
 401       constSymbol.MinValue = -20;
 402       constSymbol.ManipulatorMu = 0.0;
 403       constSymbol.ManipulatorSigma = 1;
 404       constSymbol.MultiplicativeManipulatorSigma = 0.03;
 405       symbClassProblem.SymbolicExpressionTreeGrammar = grammar;
 406
 407       // configure remaining problem parameters
 408       symbClassProblem.BestKnownQuality.Value = 0.0;
 409       symbClassProblem.FitnessCalculationPartition.Start = 0;
 410       symbClassProblem.FitnessCalculationPartition.End = 400;
 411       symbClassProblem.ValidationPartition.Start = 400;
 412       symbClassProblem.ValidationPartition.End = 800;
 413       symbClassProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
 414       symbClassProblem.MaximumSymbolicExpressionTreeLength.Value = 100;
 415       symbClassProblem.MaximumSymbolicExpressionTreeDepth.Value = 10;
 416       symbClassProblem.MaximumFunctionDefinitions.Value = 0;
 417       symbClassProblem.MaximumFunctionArguments.Value = 0;
 418       symbClassProblem.EvaluatorParameter.Value = new SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator();
 419       #endregion
 420       #region algorithm configuration
 421       ga.Problem = symbClassProblem;
 422       ga.Name = "Genetic Programming - Symbolic Classification";
 423       ga.Description = "A standard genetic programming algorithm to solve a classification problem (Mammographic+Mass dataset)";
 424       ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
 425         ga, 1000, 1, 100, 0.15, 5
 426         );
 427
 428       var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
 429       mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
 430       mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
 431
 432       ga.Analyzer.Operators.SetItemCheckedState(
 433         ga.Analyzer.Operators
 434         .OfType<SymbolicClassificationSingleObjectiveOverfittingAnalyzer>()
 435         .Single(), false);
 436       ga.Analyzer.Operators.SetItemCheckedState(
 437         ga.Analyzer.Operators
 438         .OfType<SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
 439         .First(), false);
 440       #endregion
 441       return ga;
 442     }
 443     #endregion
 444     #endregion
 445
 446     #region ES
 447     #region Griewank
 448     [TestMethod]
 449     public void CreateEsGriewankSampleTest() {
 450       var es = CreateEsGriewankSample();
 451       XmlGenerator.Serialize(es, "../../ES_Griewank.hl");
 452     }
 453     [TestMethod]
 454     public void RunEsGriewankSampleTest() {
 455       var es = CreateEsGriewankSample();
 456       es.SetSeedRandomly.Value = false;
 457       RunAlgorithm(es);
 458       Assert.AreEqual(0, GetDoubleResult(es, "BestQuality"));
 459       Assert.AreEqual(0, GetDoubleResult(es, "CurrentAverageQuality"));
 460       Assert.AreEqual(0, GetDoubleResult(es, "CurrentWorstQuality"));
 461       Assert.AreEqual(100020, GetIntResult(es, "EvaluatedSolutions"));
 462     }
 463
 464     private EvolutionStrategy CreateEsGriewankSample() {
 465       EvolutionStrategy es = new EvolutionStrategy();
 466       #region problem configuration
 467       SingleObjectiveTestFunctionProblem problem = new SingleObjectiveTestFunctionProblem();
 468
 469       problem.ProblemSize.Value = 10;
 470       problem.Evaluator = new GriewankEvaluator();
 471       problem.SolutionCreator = new UniformRandomRealVectorCreator();
 472       problem.Maximization.Value = false;
 473       problem.Bounds = new DoubleMatrix(new double[,] { { -600, 600 } });
 474       problem.BestKnownQuality.Value = 0;
 475       problem.BestKnownSolutionParameter.Value = new RealVector(10);
 476       problem.Name = "Single Objective Test Function";
 477       problem.Description = "Test function with real valued inputs and a single objective.";
 478       #endregion
 479       #region algorithm configuration
 480       es.Name = "Evolution Strategy - Griewank";
 481       es.Description = "An evolution strategy which solves the 10-dimensional Griewank test function";
 482       es.Problem = problem;
 483       ConfigureEvolutionStrategyParameters<AverageCrossover, NormalAllPositionsManipulator,
 484         StdDevStrategyVectorCreator, StdDevStrategyVectorCrossover, StdDevStrategyVectorManipulator>(
 485         es, 20, 500, 2, 200, false);
 486
 487       StdDevStrategyVectorCreator strategyCreator = (StdDevStrategyVectorCreator)es.StrategyParameterCreator;
 488       strategyCreator.BoundsParameter.Value = new DoubleMatrix(new double[,] { { 1, 20 } });
 489
 490       StdDevStrategyVectorManipulator strategyManipulator = (StdDevStrategyVectorManipulator)es.StrategyParameterManipulator;
 491       strategyManipulator.BoundsParameter.Value = new DoubleMatrix(new double[,] { { 1E-12, 30 } });
 492       strategyManipulator.GeneralLearningRateParameter.Value = new DoubleValue(0.22360679774997896);
 493       strategyManipulator.LearningRateParameter.Value = new DoubleValue(0.39763536438352531);
 494       #endregion
 495       return es;
 496     }
 497
 498     #endregion
 499     #endregion
 500
 501     #region Island GA
 502     #region TSP
 503     [TestMethod]
 504     public void CreateIslandGaTspSampleTest() {
 505       var ga = CreateIslandGaTspSample();
 506       XmlGenerator.Serialize(ga, "../../IslandGA_TSP.hl");
 507     }
 508     [TestMethod]
 509     public void RunIslandGaTspSampleTest() {
 510       var ga = CreateIslandGaTspSample();
 511       ga.SetSeedRandomly.Value = false;
 512       RunAlgorithm(ga);
 513       Assert.AreEqual(10469, GetDoubleResult(ga, "BestQuality"));
 514       Assert.AreEqual(11184.87, GetDoubleResult(ga, "CurrentAverageQuality"));
 515       Assert.AreEqual(13420, GetDoubleResult(ga, "CurrentWorstQuality"));
 516       Assert.AreEqual(495500, GetIntResult(ga, "EvaluatedSolutions"));
 517     }
 518
 519     private IslandGeneticAlgorithm CreateIslandGaTspSample() {
 520       IslandGeneticAlgorithm ga = new IslandGeneticAlgorithm();
 521       #region problem configuration
 522       TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
 523       tspProblem.ImportFromTSPLIB("ch130.tsp", "ch130.opt.tour", 6110);
 524       tspProblem.Evaluator = new TSPRoundedEuclideanPathEvaluator();
 525       tspProblem.SolutionCreator = new RandomPermutationCreator();
 526       tspProblem.UseDistanceMatrix.Value = true;
 527       tspProblem.Name = "ch130 TSP (imported from TSPLIB)";
 528       tspProblem.Description = "130 city problem (Churritz)";
 529       #endregion
 530       #region algorithm configuration
 531       ga.Name = "Island Genetic Algorithm - TSP";
 532       ga.Description = "An island genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
 533       ga.Problem = tspProblem;
 534       ConfigureIslandGeneticAlgorithmParameters<ProportionalSelector, OrderCrossover2, InversionManipulator,
 535         UnidirectionalRingMigrator, BestSelector, WorstReplacer>(
 536         ga, 100, 1, 1000, 0.05, 5, 50, 0.25);
 537
 538       ga.Analyzer.Operators.SetItemCheckedState(ga.Analyzer.Operators
 539         .OfType<TSPAlleleFrequencyAnalyzer>()
 540         .Single(), false);
 541       ga.Analyzer.Operators.SetItemCheckedState(ga.Analyzer.Operators
 542         .OfType<TSPPopulationDiversityAnalyzer>()
 543         .Single(), false);
 544       #endregion
 545       return ga;
 546     }
 547
 548     #endregion
 549     #endregion
 550
 551     #region LS
 552     #region Knapsack
 553     [TestMethod]
 554     public void CreateLocalSearchKnapsackSampleTest() {
 555       var ls = CreateLocalSearchKnapsackSample();
 556       XmlGenerator.Serialize(ls, "../../LS_Knapsack.hl");
 557     }
 558     [TestMethod]
 559     public void RunLocalSearchKnapsackSampleTest() {
 560       var ls = CreateLocalSearchKnapsackSample();
 561       ls.SetSeedRandomly.Value = false;
 562       RunAlgorithm(ls);
 563       Assert.AreEqual(345, GetDoubleResult(ls, "BestQuality"));
 564       Assert.AreEqual(340.70731707317071, GetDoubleResult(ls, "CurrentAverageQuality"));
 565       Assert.AreEqual(337, GetDoubleResult(ls, "CurrentWorstQuality"));
 566       Assert.AreEqual(82000, GetIntResult(ls, "EvaluatedMoves"));
 567     }
 568
 569     private LocalSearch CreateLocalSearchKnapsackSample() {
 570       LocalSearch ls = new LocalSearch();
 571       #region problem configuration
 572       KnapsackProblem problem = new KnapsackProblem();
 573       problem.BestKnownQuality = new DoubleValue(362);
 574       problem.BestKnownSolution = new HeuristicLab.Encodings.BinaryVectorEncoding.BinaryVector(new bool[] {
 575        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});
 576       problem.Evaluator = new KnapsackEvaluator();
 577       problem.SolutionCreator = new RandomBinaryVectorCreator();
 578       problem.KnapsackCapacity.Value = 297;
 579       problem.Maximization.Value = true;
 580       problem.Penalty.Value = 1;
 581       problem.Values = new IntArray(new int[] {
 582   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});
 583       problem.Weights = new IntArray(new int[] {
 584  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});
 585       problem.Name = "Knapsack Problem";
 586       problem.Description = "Represents a Knapsack problem.";
 587       #endregion
 588       #region algorithm configuration
 589       ls.Name = "Local Search - Knapsack";
 590       ls.Description = "A local search algorithm that solves a randomly generated Knapsack problem";
 591       ls.Problem = problem;
 592       ls.MaximumIterations.Value = 1000;
 593       ls.MoveEvaluator = ls.MoveEvaluatorParameter.ValidValues
 594         .OfType<KnapsackOneBitflipMoveEvaluator>()
 595         .Single();
 596       ls.MoveGenerator = ls.MoveGeneratorParameter.ValidValues
 597         .OfType<ExhaustiveOneBitflipMoveGenerator>()
 598         .Single();
 599       ls.MoveMaker = ls.MoveMakerParameter.ValidValues
 600         .OfType<OneBitflipMoveMaker>()
 601         .Single();
 602       ls.SampleSize.Value = 100;
 603       ls.Seed.Value = 0;
 604       ls.SetSeedRandomly.Value = true;
 605
 606       #endregion
 607       ls.Engine = new ParallelEngine();
 608       return ls;
 609     }
 610
 611     #endregion
 612     #endregion
 613
 614     #region PSO
 615     #region Schwefel
 616     [TestMethod]
 617     public void CreatePsoSchwefelSampleTest() {
 618       var pso = CreatePsoSchwefelSample();
 619       XmlGenerator.Serialize(pso, "../../PSO_Schwefel.hl");
 620     }
 621     [TestMethod]
 622     public void RunPsoSchwefelSampleTest() {
 623       var pso = CreatePsoSchwefelSample();
 624       pso.SetSeedRandomly.Value = false;
 625       RunAlgorithm(pso);
 626       Assert.AreEqual(119.33873327772062, GetDoubleResult(pso, "BestQuality"));
 627       Assert.AreEqual(140.71570105946438, GetDoubleResult(pso, "CurrentAverageQuality"));
 628       Assert.AreEqual(220.956806502853, GetDoubleResult(pso, "CurrentWorstQuality"));
 629       Assert.AreEqual(1000, GetIntResult(pso, "Iterations"));
 630     }
 631     private ParticleSwarmOptimization CreatePsoSchwefelSample() {
 632       ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
 633       #region problem configuration
 634       var problem = new SingleObjectiveTestFunctionProblem();
 635       problem.BestKnownQuality.Value = 0.0;
 636       problem.BestKnownSolutionParameter.Value = new RealVector(new double[] { 420.968746, 420.968746 });
 637       problem.Bounds = new DoubleMatrix(new double[,] { { -500, 500 } });
 638       problem.Evaluator = new SchwefelEvaluator();
 639       problem.Maximization.Value = false;
 640       problem.ProblemSize.Value = 2;
 641       problem.SolutionCreator = new UniformRandomRealVectorCreator();
 642       #endregion
 643       #region algorithm configuration
 644       pso.Name = "Particle Swarm Optimization - Schwefel";
 645       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)";
 646       pso.Problem = problem;
 647       pso.Inertia.Value = 10;
 648       pso.MaxIterations.Value = 1000;
 649       pso.NeighborBestAttraction.Value = 0.5;
 650       pso.PersonalBestAttraction.Value = -0.01;
 651       pso.SwarmSize.Value = 50;
 652
 653       var inertiaUpdater = pso.InertiaUpdaterParameter.ValidValues
 654         .OfType<ExponentialDiscreteDoubleValueModifier>()
 655         .Single();
 656       inertiaUpdater.StartValueParameter.Value = new DoubleValue(10);
 657       inertiaUpdater.EndValueParameter.Value = new DoubleValue(1);
 658       pso.InertiaUpdater = inertiaUpdater;
 659
 660       pso.ParticleCreator = pso.ParticleCreatorParameter.ValidValues
 661         .OfType<RealVectorParticleCreator>()
 662         .Single();
 663       var swarmUpdater = pso.SwarmUpdaterParameter.ValidValues
 664         .OfType<RealVectorSwarmUpdater>()
 665         .Single();
 666       swarmUpdater.VelocityBoundsIndexParameter.ActualName = "Iterations";
 667       swarmUpdater.VelocityBoundsParameter.Value = new DoubleMatrix(new double[,] { { -10, 10 } });
 668       swarmUpdater.VelocityBoundsStartValueParameter.Value = new DoubleValue(10.0);
 669       swarmUpdater.VelocityBoundsEndValueParameter.Value = new DoubleValue(1.0);
 670       swarmUpdater.VelocityBoundsScalingOperatorParameter.Value = swarmUpdater.VelocityBoundsScalingOperatorParameter.ValidValues
 671         .OfType<ExponentialDiscreteDoubleValueModifier>()
 672         .Single();
 673
 674       pso.TopologyInitializer = null;
 675       pso.TopologyUpdater = null;
 676       pso.SwarmUpdater = swarmUpdater;
 677       pso.Seed.Value = 0;
 678       pso.SetSeedRandomly.Value = true;
 679
 680       #endregion
 681       pso.Engine = new ParallelEngine();
 682       return pso;
 683     }
 684     #endregion
 685     #endregion
 686
 687     #region SA
 688     #region Rastrigin
 689     [TestMethod]
 690     public void CreateSimulatedAnnealingRastriginSampleTest() {
 691       var sa = CreateSimulatedAnnealingRastriginSample();
 692       XmlGenerator.Serialize(sa, "../../SA_Rastrigin.hl");
 693     }
 694     [TestMethod]
 695     public void RunSimulatedAnnealingRastriginSampleTest() {
 696       var sa = CreateSimulatedAnnealingRastriginSample();
 697       sa.SetSeedRandomly.Value = false;
 698       RunAlgorithm(sa);
 699       Assert.AreEqual(0.00014039606034543795, GetDoubleResult(sa, "BestQuality"));
 700       Assert.AreEqual(5000, GetIntResult(sa, "EvaluatedMoves"));
 701     }
 702     private SimulatedAnnealing CreateSimulatedAnnealingRastriginSample() {
 703       SimulatedAnnealing sa = new SimulatedAnnealing();
 704       #region problem configuration
 705       var problem = new SingleObjectiveTestFunctionProblem();
 706       problem.BestKnownQuality.Value = 0.0;
 707       problem.BestKnownSolutionParameter.Value = new RealVector(new double[] { 0, 0 });
 708       problem.Bounds = new DoubleMatrix(new double[,] { { -5.12, 5.12 } });
 709       problem.Evaluator = new RastriginEvaluator();
 710       problem.Maximization.Value = false;
 711       problem.ProblemSize.Value = 2;
 712       problem.SolutionCreator = new UniformRandomRealVectorCreator();
 713       #endregion
 714       #region algorithm configuration
 715       sa.Name = "Simulated Annealing - Rastrigin";
 716       sa.Description = "A simulated annealing algorithm that solves the 2-dimensional Rastrigin test function";
 717       sa.Problem = problem;
 718       var annealingOperator = sa.AnnealingOperatorParameter.ValidValues
 719         .OfType<ExponentialDiscreteDoubleValueModifier>()
 720         .Single();
 721       annealingOperator.StartIndexParameter.Value = new IntValue(0);
 722       sa.AnnealingOperator = annealingOperator;
 723
 724       sa.EndTemperature.Value = 1E-6;
 725       sa.InnerIterations.Value = 50;
 726       sa.MaximumIterations.Value = 100;
 727       var moveEvaluator = sa.MoveEvaluatorParameter.ValidValues
 728         .OfType<RastriginAdditiveMoveEvaluator>()
 729         .Single();
 730       moveEvaluator.A.Value = 10;
 731       sa.MoveEvaluator = moveEvaluator;
 732
 733       var moveGenerator = sa.MoveGeneratorParameter.ValidValues
 734         .OfType<StochasticNormalMultiMoveGenerator>()
 735         .Single();
 736       moveGenerator.SigmaParameter.Value = new DoubleValue(1);
 737       sa.MoveGenerator = moveGenerator;
 738
 739       sa.MoveMaker = sa.MoveMakerParameter.ValidValues
 740         .OfType<AdditiveMoveMaker>()
 741         .Single();
 742
 743       sa.Seed.Value = 0;
 744       sa.SetSeedRandomly.Value = true;
 745       sa.StartTemperature.Value = 1;
 746       #endregion
 747       sa.Engine = new ParallelEngine();
 748       return sa;
 749     }
 750     #endregion
 751     #endregion
 752
 753     #region TS
 754     #region TSP
 755     [TestMethod]
 756     public void CreateTabuSearchTspSampleTest() {
 757       var ts = CreateTabuSearchTspSample();
 758       XmlGenerator.Serialize(ts, "../../TS_TSP.hl");
 759     }
 760     [TestMethod]
 761     public void RunTabuSearchTspSampleTest() {
 762       var ts = CreateTabuSearchTspSample();
 763       ts.SetSeedRandomly.Value = false;
 764       RunAlgorithm(ts);
 765       Assert.AreEqual(6441, GetDoubleResult(ts, "BestQuality"));
 766       Assert.AreEqual(7401.666666666667, GetDoubleResult(ts, "CurrentAverageQuality"));
 767       Assert.AreEqual(8418, GetDoubleResult(ts, "CurrentWorstQuality"));
 768       Assert.AreEqual(750000, GetIntResult(ts, "EvaluatedMoves"));
 769     }
 770
 771     private TabuSearch CreateTabuSearchTspSample() {
 772       TabuSearch ts = new TabuSearch();
 773       #region problem configuration
 774       var tspProblem = new TravelingSalesmanProblem();
 775       tspProblem.ImportFromTSPLIB("ch130.tsp", "ch130.opt.tour", 6110);
 776       tspProblem.Evaluator = new TSPRoundedEuclideanPathEvaluator();
 777       tspProblem.SolutionCreator = new RandomPermutationCreator();
 778       tspProblem.UseDistanceMatrix.Value = true;
 779       tspProblem.Name = "ch130 TSP (imported from TSPLIB)";
 780       tspProblem.Description = "130 city problem (Churritz)";
 781       #endregion
 782       #region algorithm configuration
 783       ts.Name = "Tabu Search - TSP";
 784       ts.Description = "A tabu search algorithm that solves the \"ch130\" TSP (imported from TSPLIB)";
 785       ts.Problem = tspProblem;
 786
 787       ts.MaximumIterations.Value = 1000;
 788       // move generator has to be set first
 789       var moveGenerator = ts.MoveGeneratorParameter.ValidValues
 790         .OfType<StochasticInversionMultiMoveGenerator>()
 791         .Single();
 792       ts.MoveGenerator = moveGenerator;
 793       var moveEvaluator = ts.MoveEvaluatorParameter.ValidValues
 794         .OfType<TSPInversionMoveRoundedEuclideanPathEvaluator>()
 795         .Single();
 796       ts.MoveEvaluator = moveEvaluator;
 797       var moveMaker = ts.MoveMakerParameter.ValidValues
 798         .OfType<InversionMoveMaker>()
 799         .Single();
 800       ts.MoveMaker = moveMaker;
 801       ts.SampleSize.Value = 750;
 802       ts.Seed.Value = 0;
 803       ts.SetSeedRandomly.Value = true;
 804
 805       var tabuChecker = ts.TabuCheckerParameter.ValidValues
 806         .OfType<InversionMoveSoftTabuCriterion>()
 807         .Single();
 808       tabuChecker.UseAspirationCriterion.Value = true;
 809       ts.TabuChecker = tabuChecker;
 810
 811       var tabuMaker = ts.TabuMakerParameter.ValidValues
 812         .OfType<InversionMoveTabuMaker>()
 813         .Single();
 814       ts.TabuMaker = tabuMaker;
 815       ts.TabuTenure.Value = 60;
 816
 817       ts.Analyzer.Operators.SetItemCheckedState(ts.Analyzer.Operators
 818         .OfType<TSPAlleleFrequencyAnalyzer>()
 819         .Single(), false);
 820       ts.Analyzer.Operators.SetItemCheckedState(ts.Analyzer.Operators
 821         .OfType<TSPPopulationDiversityAnalyzer>()
 822         .Single(), false);
 823       #endregion
 824       ts.Engine = new ParallelEngine();
 825       return ts;
 826     }
 827
 828     #endregion
 829     #endregion
 830
 831     #region VNS
 832     #region TSP
 833     [TestMethod]
 834     public void CreateVnsTspSampleTest() {
 835       var vns = CreateVnsTspSample();
 836       XmlGenerator.Serialize(vns, "../../VNS_TSP.hl");
 837     }
 838     [TestMethod]
 839     public void RunVnsTspSampleTest() {
 840       var vns = CreateVnsTspSample();
 841       vns.SetSeedRandomly = false;
 842       RunAlgorithm(vns);
 843       Assert.AreEqual(867, GetDoubleResult(vns, "BestQuality"));
 844       Assert.AreEqual(867, GetDoubleResult(vns, "CurrentAverageQuality"));
 845       Assert.AreEqual(867, GetDoubleResult(vns, "CurrentWorstQuality"));
 846       Assert.AreEqual(12975173, GetIntResult(vns, "EvaluatedMoves"));
 847     }
 848
 849     private VariableNeighborhoodSearch CreateVnsTspSample() {
 850       VariableNeighborhoodSearch vns = new VariableNeighborhoodSearch();
 851       #region problem configuration
 852       TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
 853       tspProblem.BestKnownSolution = new Permutation(PermutationTypes.Absolute, new int[] {
 854 117, 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,
 855       });
 856       tspProblem.Coordinates = new DoubleMatrix(new double[,] {
 857 {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}
 858       });
 859       tspProblem.BestKnownQuality = new DoubleValue(867);
 860
 861       tspProblem.Evaluator = new TSPRoundedEuclideanPathEvaluator();
 862       tspProblem.SolutionCreator = new RandomPermutationCreator();
 863       tspProblem.UseDistanceMatrix.Value = true;
 864       tspProblem.Name = "Funny TSP";
 865       tspProblem.Description = "Represents a symmetric Traveling Salesman Problem.";
 866       #endregion
 867       #region algorithm configuration
 868       vns.Name = "Variable Neighborhood Search - TSP";
 869       vns.Description = "A variable neighborhood search algorithm which solves a funny TSP instance";
 870       vns.Problem = tspProblem;
 871
 872       var localImprovement = vns.LocalImprovementParameter.ValidValues
 873         .OfType<LocalSearchImprovementOperator>()
 874         .Single();
 875       // move generator has to be set first
 876       localImprovement.MoveGenerator = localImprovement.MoveGeneratorParameter.ValidValues
 877         .OfType<StochasticInversionMultiMoveGenerator>()
 878         .Single();
 879       localImprovement.MoveEvaluator = localImprovement.MoveEvaluatorParameter.ValidValues
 880         .OfType<TSPInversionMoveRoundedEuclideanPathEvaluator>()
 881         .Single();
 882       localImprovement.MoveMaker = localImprovement.MoveMakerParameter.ValidValues
 883         .OfType<InversionMoveMaker>()
 884         .Single();
 885       localImprovement.SampleSizeParameter.Value = new IntValue(500);
 886       vns.LocalImprovement = localImprovement;
 887
 888       vns.LocalImprovementMaximumIterations = 150;
 889       vns.MaximumIterations = 25;
 890       vns.Seed = 0;
 891       vns.SetSeedRandomly = true;
 892       var shakingOperator = vns.ShakingOperatorParameter.ValidValues
 893         .OfType<PermutationShakingOperator>()
 894         .Single();
 895       shakingOperator.Operators.SetItemCheckedState(shakingOperator.Operators
 896         .OfType<Swap2Manipulator>()
 897         .Single(), false);
 898       shakingOperator.Operators.SetItemCheckedState(shakingOperator.Operators
 899         .OfType<Swap3Manipulator>()
 900         .Single(), false);
 901       vns.ShakingOperator = shakingOperator;
 902       vns.Analyzer.Operators.SetItemCheckedState(vns.Analyzer.Operators
 903         .OfType<TSPAlleleFrequencyAnalyzer>()
 904         .Single(), false);
 905       vns.Analyzer.Operators.SetItemCheckedState(vns.Analyzer.Operators
 906         .OfType<TSPPopulationDiversityAnalyzer>()
 907         .Single(), false);
 908       #endregion
 909       vns.Engine = new ParallelEngine();
 910       return vns;
 911     }
 912
 913     #endregion
 914     #endregion
 915     #region helper
 916     private void ConfigureEvolutionStrategyParameters<R, M, SC, SR, SM>(EvolutionStrategy es, int popSize, int children, int parentsPerChild, int maxGens, bool plusSelection)
 917       where R : ICrossover
 918       where M : IManipulator
 919       where SC : IStrategyParameterCreator
 920       where SR : IStrategyParameterCrossover
 921       where SM : IStrategyParameterManipulator {
 922       es.PopulationSize.Value = popSize;
 923       es.Children.Value = children;
 924       es.ParentsPerChild.Value = parentsPerChild;
 925       es.MaximumGenerations.Value = maxGens;
 926       es.PlusSelection.Value = false;
 927
 928       es.Seed.Value = 0;
 929       es.SetSeedRandomly.Value = true;
 930
 931       es.Recombinator = es.RecombinatorParameter.ValidValues
 932         .OfType<R>()
 933         .Single();
 934
 935       es.Mutator = es.MutatorParameter.ValidValues
 936         .OfType<M>()
 937         .Single();
 938
 939       es.StrategyParameterCreator = es.StrategyParameterCreatorParameter.ValidValues
 940         .OfType<SC>()
 941         .Single();
 942       es.StrategyParameterCrossover = es.StrategyParameterCrossoverParameter.ValidValues
 943         .OfType<SR>()
 944         .Single();
 945       es.StrategyParameterManipulator = es.StrategyParameterManipulatorParameter.ValidValues
 946         .OfType<SM>()
 947         .Single();
 948       es.Engine = new ParallelEngine();
 949     }
 950
 951     private void ConfigureGeneticAlgorithmParameters<S, C, M>(GeneticAlgorithm ga, int popSize, int elites, int maxGens, double mutationRate, int tournGroupSize = 0)
 952       where S : ISelector
 953       where C : ICrossover
 954       where M : IManipulator {
 955       ga.Elites.Value = elites;
 956       ga.MaximumGenerations.Value = maxGens;
 957       ga.MutationProbability.Value = mutationRate;
 958       ga.PopulationSize.Value = popSize;
 959       ga.Seed.Value = 0;
 960       ga.SetSeedRandomly.Value = true;
 961       ga.Selector = ga.SelectorParameter.ValidValues
 962         .OfType<S>()
 963         .Single();
 964
 965       ga.Crossover = ga.CrossoverParameter.ValidValues
 966         .OfType<C>()
 967         .Single();
 968
 969       ga.Mutator = ga.MutatorParameter.ValidValues
 970         .OfType<M>()
 971         .Single();
 972
 973       var tSelector = ga.Selector as TournamentSelector;
 974       if (tSelector != null) {
 975         tSelector.GroupSizeParameter.Value.Value = tournGroupSize;
 976       }
 977       ga.Engine = new ParallelEngine();
 978     }
 979
 980     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)
 981       where S : ISelector
 982       where C : ICrossover
 983       where M : IManipulator
 984       where Mi : IMigrator
 985       where MiS : ISelector
 986       where MiR : IReplacer {
 987       ga.Elites.Value = elites;
 988       ga.MaximumGenerations.Value = maxGens;
 989       ga.MutationProbability.Value = mutationRate;
 990       ga.PopulationSize.Value = popSize;
 991       ga.NumberOfIslands.Value = numberOfIslands;
 992       ga.MigrationInterval.Value = migrationInterval;
 993       ga.MigrationRate.Value = migrationRate;
 994       ga.Seed.Value = 0;
 995       ga.SetSeedRandomly.Value = true;
 996       ga.Selector = ga.SelectorParameter.ValidValues
 997         .OfType<S>()
 998         .Single();
 999
1000       ga.Crossover = ga.CrossoverParameter.ValidValues
1001         .OfType<C>()
1002         .Single();
1003
1004       ga.Mutator = ga.MutatorParameter.ValidValues
1005         .OfType<M>()
1006         .Single();
1007       ga.Migrator = ga.MigratorParameter.ValidValues
1008         .OfType<Mi>()
1009         .Single();
1010       ga.EmigrantsSelector = ga.EmigrantsSelectorParameter.ValidValues
1011         .OfType<MiS>()
1012         .Single();
1013       ga.ImmigrationReplacer = ga.ImmigrationReplacerParameter.ValidValues
1014         .OfType<MiR>()
1015         .Single();
1016       ga.Engine = new ParallelEngine();
1017     }
1018
1019
1020     private void RunAlgorithm(IAlgorithm a) {
1021       var trigger = new EventWaitHandle(false, EventResetMode.ManualReset);
1022       Exception ex = null;
1023       a.Stopped += (src, e) => { trigger.Set(); };
1024       a.ExceptionOccurred += (src, e) => { ex = e.Value; trigger.Set(); };
1025       a.Prepare();
1026       a.Start();
1027       trigger.WaitOne();
1028
1029       Assert.AreEqual(ex, null);
1030     }
1031
1032     private double GetDoubleResult(IAlgorithm a, string resultName) {
1033       return ((DoubleValue)a.Results[resultName].Value).Value;
1034     }
1035     private int GetIntResult(IAlgorithm a, string resultName) {
1036       return ((IntValue)a.Results[resultName].Value).Value;
1037     }
1038     #endregion
1039   }
1040 }