Free cookie consent management tool by TermsFeed Policy Generator

source: trunk/sources/HeuristicLab.Tests/HeuristicLab-3.3/SamplesTest.cs @ 7915

Last change on this file since 7915 was 7915, checked in by mkommend, 12 years ago

#1777: Extracted unit tests into a separate solution.
File size: 55.0 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Linq;
24using System.Threading;
25using HeuristicLab.Algorithms.EvolutionStrategy;
26using HeuristicLab.Algorithms.GeneticAlgorithm;
27using HeuristicLab.Algorithms.LocalSearch;
28using HeuristicLab.Algorithms.ParticleSwarmOptimization;
29using HeuristicLab.Algorithms.SimulatedAnnealing;
30using HeuristicLab.Algorithms.TabuSearch;
31using HeuristicLab.Algorithms.VariableNeighborhoodSearch;
32using HeuristicLab.Data;
33using HeuristicLab.Encodings.BinaryVectorEncoding;
34using HeuristicLab.Encodings.PermutationEncoding;
35using HeuristicLab.Encodings.RealVectorEncoding;
36using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
37using HeuristicLab.Optimization;
38using HeuristicLab.Optimization.Operators;
39using HeuristicLab.ParallelEngine;
40using HeuristicLab.Persistence.Default.Xml;
41using HeuristicLab.Problems.ArtificialAnt;
42using HeuristicLab.Problems.DataAnalysis;
43using HeuristicLab.Problems.DataAnalysis.Symbolic;
44using HeuristicLab.Problems.DataAnalysis.Symbolic.Classification;
45using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
46using HeuristicLab.Problems.Instances.DataAnalysis;
47using HeuristicLab.Problems.Instances.TSPLIB;
48using HeuristicLab.Problems.Knapsack;
49using HeuristicLab.Problems.TestFunctions;
50using HeuristicLab.Problems.TravelingSalesman;
51using HeuristicLab.Problems.VehicleRouting;
52using HeuristicLab.Problems.VehicleRouting.Encodings.General;
53using HeuristicLab.Problems.VehicleRouting.Encodings.Alba;
54using HeuristicLab.Problems.VehicleRouting.Encodings.Potvin;
55using HeuristicLab.Problems.VehicleRouting.ProblemInstances;
56using HeuristicLab.Selection;
57using Microsoft.VisualStudio.TestTools.UnitTesting;
58using HeuristicLab.Problems.Instances.VehicleRouting;
59using HeuristicLab.Problems.Instances;
60
61
62namespace HeuristicLab_33.Tests {
63  [TestClass]
64  [DeploymentItem(@"HeuristicLab-3.3/Resources/C101.opt.txt")]
65  [DeploymentItem(@"HeuristicLab-3.3/Resources/C101.txt")]
66  public class SamplesTest {
67    #region GA
68    #region TSP
69    [TestMethod]
70    public void CreateGaTspSampleTest() {
71      var ga = CreateGaTspSample();
72      XmlGenerator.Serialize(ga, "../../GA_TSP.hl");
73    }
74    [TestMethod]
75    public void RunGaTspSampleTest() {
76      var ga = CreateGaTspSample();
77      ga.SetSeedRandomly.Value = false;
78      RunAlgorithm(ga);
79      Assert.AreEqual(12332, GetDoubleResult(ga, "BestQuality"));
80      Assert.AreEqual(13123.2, GetDoubleResult(ga, "CurrentAverageQuality"));
81      Assert.AreEqual(14538, GetDoubleResult(ga, "CurrentWorstQuality"));
82      Assert.AreEqual(99100, GetIntResult(ga, "EvaluatedSolutions"));
83    }
84
85    private GeneticAlgorithm CreateGaTspSample() {
86      GeneticAlgorithm ga = new GeneticAlgorithm();
87      #region Problem Configuration
88      var provider = new TSPLIBTSPInstanceProvider();
89      var instance = provider.GetDataDescriptors().Where(x => x.Name == "ch130").Single();
90      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
91      tspProblem.Load(provider.LoadData(instance));
92      tspProblem.UseDistanceMatrix.Value = true;
93      #endregion
94      #region Algorithm Configuration
95      ga.Name = "Genetic Algorithm - TSP";
96      ga.Description = "A genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
97      ga.Problem = tspProblem;
98      ConfigureGeneticAlgorithmParameters<ProportionalSelector, OrderCrossover2, InversionManipulator>(
99        ga, 100, 1, 1000, 0.05);
100
101      ga.Analyzer.Operators.SetItemCheckedState(ga.Analyzer.Operators
102        .OfType<TSPAlleleFrequencyAnalyzer>()
103        .Single(), false);
104      ga.Analyzer.Operators.SetItemCheckedState(ga.Analyzer.Operators
105        .OfType<TSPPopulationDiversityAnalyzer>()
106        .Single(), false);
107      #endregion
108      return ga;
109    }
110    #endregion
111    #region VRP
112    [TestMethod]
113    public void CreateGaVrpSampleTest() {
114      var ga = CreateGaVrpSample();
115      XmlGenerator.Serialize(ga, "../../GA_VRP.hl");
116    }
117
118    [TestMethod]
119    public void RunGaVrpSampleTest() {
120      var ga = CreateGaVrpSample();
121      ga.SetSeedRandomly.Value = false;
122      RunAlgorithm(ga);
123      Assert.AreEqual(1828.9368669428338, GetDoubleResult(ga, "BestQuality"));
124      Assert.AreEqual(1830.1444308908331, GetDoubleResult(ga, "CurrentAverageQuality"));
125      Assert.AreEqual(1871.7128510304112, GetDoubleResult(ga, "CurrentWorstQuality"));
126      Assert.AreEqual(99100, GetIntResult(ga, "EvaluatedSolutions"));
127    }
128
129    private GeneticAlgorithm CreateGaVrpSample() {
130      GeneticAlgorithm ga = new GeneticAlgorithm();
131      #region Problem Configuration
132      VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
133
134      SolomonFormatInstanceProvider instanceProvider = new SolomonInstanceProvider();
135      IVRPData data = instanceProvider.LoadData("C101.txt", "C101.opt.txt");
136      vrpProblem.Load(data);
137      vrpProblem.Name = "C101 VRP (imported from Solomon)";
138      vrpProblem.Description = "Represents a Vehicle Routing Problem.";
139      CVRPTWProblemInstance instance = vrpProblem.ProblemInstance as CVRPTWProblemInstance;
140      instance.DistanceFactor.Value = 1;
141      instance.FleetUsageFactor.Value = 100;
142      instance.OverloadPenalty.Value = 100;
143      instance.TardinessPenalty.Value = 100;
144      instance.TimeFactor.Value = 0;
145      vrpProblem.MaximizationParameter.Value.Value = false;
146      instance.UseDistanceMatrix.Value = true;
147      instance.Vehicles.Value = 25;
148      #endregion
149      #region Algorithm Configuration
150      ga.Name = "Genetic Algorithm - VRP";
151      ga.Description = "A genetic algorithm which solves the \"C101\" vehicle routing problem (imported from Solomon)";
152      ga.Problem = vrpProblem;
153      ConfigureGeneticAlgorithmParameters<TournamentSelector, MultiVRPSolutionCrossover, MultiVRPSolutionManipulator>(
154        ga, 100, 1, 1000, 0.05, 3);
155
156      var xOver = (MultiVRPSolutionCrossover)ga.Crossover;
157      foreach (var op in xOver.Operators) {
158        xOver.Operators.SetItemCheckedState(op, false);
159      }
160      xOver.Operators.SetItemCheckedState(xOver.Operators
161        .OfType<PotvinRouteBasedCrossover>()
162        .Single(), true);
163      xOver.Operators.SetItemCheckedState(xOver.Operators
164        .OfType<PotvinSequenceBasedCrossover>()
165        .Single(), true);
166
167      var manipulator = (MultiVRPSolutionManipulator)ga.Mutator;
168      foreach (var op in manipulator.Operators) {
169        manipulator.Operators.SetItemCheckedState(op, false);
170      }
171      manipulator.Operators.SetItemCheckedState(manipulator.Operators
172        .OfType<PotvinOneLevelExchangeMainpulator>()
173        .Single(), true);
174      manipulator.Operators.SetItemCheckedState(manipulator.Operators
175        .OfType<PotvinTwoLevelExchangeManipulator>()
176        .Single(), true);
177      #endregion
178      return ga;
179    }
180    #endregion
181    #region ArtificialAnt
182    [TestMethod]
183    public void CreateGpArtificialAntSampleTest() {
184      var ga = CreateGpArtificialAntSample();
185      XmlGenerator.Serialize(ga, "../../SGP_SantaFe.hl");
186    }
187
188    [TestMethod]
189    public void RunGpArtificialAntSampleTest() {
190      var ga = CreateGpArtificialAntSample();
191      ga.SetSeedRandomly.Value = false;
192      RunAlgorithm(ga);
193      Assert.AreEqual(67, GetDoubleResult(ga, "BestQuality"));
194      Assert.AreEqual(45.813, GetDoubleResult(ga, "CurrentAverageQuality"));
195      Assert.AreEqual(0, GetDoubleResult(ga, "CurrentWorstQuality"));
196      Assert.AreEqual(50950, GetIntResult(ga, "EvaluatedSolutions"));
197    }
198
199    public GeneticAlgorithm CreateGpArtificialAntSample() {
200      GeneticAlgorithm ga = new GeneticAlgorithm();
201      #region Problem Configuration
202      ArtificialAntProblem antProblem = new ArtificialAntProblem();
203      antProblem.BestKnownQuality.Value = 89;
204      antProblem.MaxExpressionDepth.Value = 10;
205      antProblem.MaxExpressionLength.Value = 100;
206      antProblem.MaxFunctionArguments.Value = 3;
207      antProblem.MaxFunctionDefinitions.Value = 3;
208      antProblem.MaxTimeSteps.Value = 600;
209      #endregion
210      #region Algorithm Configuration
211      ga.Name = "Genetic Programming - Artificial Ant";
212      ga.Description = "A standard genetic programming algorithm to solve the artificial ant problem (Santa-Fe trail)";
213      ga.Problem = antProblem;
214      ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeArchitectureManipulator>(
215        ga, 1000, 1, 50, 0.15, 5);
216      var mutator = (MultiSymbolicExpressionTreeArchitectureManipulator)ga.Mutator;
217      mutator.Operators.SetItemCheckedState(mutator.Operators
218        .OfType<FullTreeShaker>()
219        .Single(), false);
220      mutator.Operators.SetItemCheckedState(mutator.Operators
221        .OfType<OnePointShaker>()
222        .Single(), false);
223      mutator.Operators.SetItemCheckedState(mutator.Operators
224        .OfType<ArgumentDeleter>()
225        .Single(), false);
226      mutator.Operators.SetItemCheckedState(mutator.Operators
227        .OfType<SubroutineDeleter>()
228        .Single(), false);
229      #endregion
230      return ga;
231    }
232    #endregion
233    #region Symbolic Regression
234    [TestMethod]
235    public void CreateGpSymbolicRegressionSampleTest() {
236      var ga = CreateGpSymbolicRegressionSample();
237      XmlGenerator.Serialize(ga, "../../SGP_SymbReg.hl");
238    }
239    [TestMethod]
240    public void RunGpSymbolicRegressionSampleTest() {
241      var ga = CreateGpSymbolicRegressionSample();
242      ga.SetSeedRandomly.Value = false;
243      RunAlgorithm(ga);
244      Assert.AreEqual(0.78855594192122458, GetDoubleResult(ga, "BestQuality"), 1E-8);
245      Assert.AreEqual(0.61395271071681523, GetDoubleResult(ga, "CurrentAverageQuality"), 1E-8);
246      Assert.AreEqual(0, GetDoubleResult(ga, "CurrentWorstQuality"), 1E-8);
247      Assert.AreEqual(50950, GetIntResult(ga, "EvaluatedSolutions"));
248    }
249
250    private GeneticAlgorithm CreateGpSymbolicRegressionSample() {
251      GeneticAlgorithm ga = new GeneticAlgorithm();
252      #region Problem Configuration
253      SymbolicRegressionSingleObjectiveProblem symbRegProblem = new SymbolicRegressionSingleObjectiveProblem();
254      symbRegProblem.Name = "Tower Symbolic Regression Problem";
255      symbRegProblem.Description = "Tower Dataset (downloaded from: http://vanillamodeling.com/realproblems.html)";
256      RegressionRealWorldInstanceProvider provider = new RegressionRealWorldInstanceProvider();
257      var instance = provider.GetDataDescriptors().Where(x => x.Name.Equals("TowerData")).Single();
258      var towerProblemData = (RegressionProblemData)provider.LoadData(instance);
259      towerProblemData.TargetVariableParameter.Value = towerProblemData.TargetVariableParameter.ValidValues
260        .First(v => v.Value == "towerResponse");
261      towerProblemData.InputVariables.SetItemCheckedState(
262        towerProblemData.InputVariables.Single(x => x.Value == "x1"), true);
263      towerProblemData.InputVariables.SetItemCheckedState(
264        towerProblemData.InputVariables.Single(x => x.Value == "x7"), false);
265      towerProblemData.InputVariables.SetItemCheckedState(
266        towerProblemData.InputVariables.Single(x => x.Value == "x11"), false);
267      towerProblemData.InputVariables.SetItemCheckedState(
268        towerProblemData.InputVariables.Single(x => x.Value == "x16"), false);
269      towerProblemData.InputVariables.SetItemCheckedState(
270        towerProblemData.InputVariables.Single(x => x.Value == "x21"), false);
271      towerProblemData.InputVariables.SetItemCheckedState(
272        towerProblemData.InputVariables.Single(x => x.Value == "x25"), false);
273      towerProblemData.InputVariables.SetItemCheckedState(
274        towerProblemData.InputVariables.Single(x => x.Value == "towerResponse"), false);
275      towerProblemData.TrainingPartition.Start = 0;
276      towerProblemData.TrainingPartition.End = 4000;
277      towerProblemData.TestPartition.Start = 4000;
278      towerProblemData.TestPartition.End = 4999;
279      towerProblemData.Name = "Data imported from towerData.txt";
280      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";
281      symbRegProblem.ProblemData = towerProblemData;
282
283      // configure grammar
284      var grammar = new TypeCoherentExpressionGrammar();
285      grammar.ConfigureAsDefaultRegressionGrammar();
286      grammar.Symbols.OfType<VariableCondition>().Single().InitialFrequency = 0.0;
287      var varSymbol = grammar.Symbols.OfType<Variable>().Where(x => !(x is LaggedVariable)).Single();
288      varSymbol.WeightMu = 1.0;
289      varSymbol.WeightSigma = 1.0;
290      varSymbol.WeightManipulatorMu = 0.0;
291      varSymbol.WeightManipulatorSigma = 0.05;
292      varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
293      var constSymbol = grammar.Symbols.OfType<Constant>().Single();
294      constSymbol.MaxValue = 20;
295      constSymbol.MinValue = -20;
296      constSymbol.ManipulatorMu = 0.0;
297      constSymbol.ManipulatorSigma = 1;
298      constSymbol.MultiplicativeManipulatorSigma = 0.03;
299      symbRegProblem.SymbolicExpressionTreeGrammar = grammar;
300
301      // configure remaining problem parameters
302      symbRegProblem.BestKnownQuality.Value = 0.97;
303      symbRegProblem.FitnessCalculationPartition.Start = 0;
304      symbRegProblem.FitnessCalculationPartition.End = 2800;
305      symbRegProblem.ValidationPartition.Start = 2800;
306      symbRegProblem.ValidationPartition.End = 4000;
307      symbRegProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
308      symbRegProblem.MaximumSymbolicExpressionTreeLength.Value = 150;
309      symbRegProblem.MaximumSymbolicExpressionTreeDepth.Value = 12;
310      symbRegProblem.MaximumFunctionDefinitions.Value = 0;
311      symbRegProblem.MaximumFunctionArguments.Value = 0;
312
313      symbRegProblem.EvaluatorParameter.Value = new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator();
314      #endregion
315      #region Algorithm Configuration
316      ga.Problem = symbRegProblem;
317      ga.Name = "Genetic Programming - Symbolic Regression";
318      ga.Description = "A standard genetic programming algorithm to solve a symbolic regression problem (tower dataset)";
319      ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
320        ga, 1000, 1, 50, 0.15, 5);
321      var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
322      mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
323      mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
324
325      ga.Analyzer.Operators.SetItemCheckedState(
326        ga.Analyzer.Operators
327        .OfType<SymbolicRegressionSingleObjectiveOverfittingAnalyzer>()
328        .Single(), false);
329      ga.Analyzer.Operators.SetItemCheckedState(
330        ga.Analyzer.Operators
331        .OfType<SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
332        .First(), false);
333      #endregion
334      return ga;
335    }
336    #endregion
337    #region Symbolic Classification
338    [TestMethod]
339    public void CreateGpSymbolicClassificationSampleTest() {
340      var ga = CreateGpSymbolicClassificationSample();
341      XmlGenerator.Serialize(ga, "../../SGP_SymbClass.hl");
342    }
343
344    [TestMethod]
345    public void RunGpSymbolicClassificationSampleTest() {
346      var ga = CreateGpSymbolicClassificationSample();
347      ga.SetSeedRandomly.Value = false;
348      RunAlgorithm(ga);
349      Assert.AreEqual(0.13775264138895371, GetDoubleResult(ga, "BestQuality"), 1E-8);
350      Assert.AreEqual(14.232802217120254, GetDoubleResult(ga, "CurrentAverageQuality"), 1E-8);
351      Assert.AreEqual(104.24339008411457, GetDoubleResult(ga, "CurrentWorstQuality"), 1E-8);
352      Assert.AreEqual(100900, GetIntResult(ga, "EvaluatedSolutions"));
353    }
354
355    private GeneticAlgorithm CreateGpSymbolicClassificationSample() {
356      GeneticAlgorithm ga = new GeneticAlgorithm();
357      #region Problem Configuration
358      SymbolicClassificationSingleObjectiveProblem symbClassProblem = new SymbolicClassificationSingleObjectiveProblem();
359      symbClassProblem.Name = "Mammography Classification Problem";
360      symbClassProblem.Description = "Mammography dataset imported from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass)";
361      ClassificationRealWorldInstanceProvider provider = new ClassificationRealWorldInstanceProvider();
362      var instance = provider.GetDataDescriptors().Where(x => x.Name.Equals("Mammography")).Single();
363      var mammoData = (ClassificationProblemData)provider.LoadData(instance);
364      mammoData.TargetVariableParameter.Value = mammoData.TargetVariableParameter.ValidValues
365        .First(v => v.Value == "Severity");
366      mammoData.InputVariables.SetItemCheckedState(
367        mammoData.InputVariables.Single(x => x.Value == "BI-RADS"), false);
368      mammoData.InputVariables.SetItemCheckedState(
369        mammoData.InputVariables.Single(x => x.Value == "Age"), true);
370      mammoData.InputVariables.SetItemCheckedState(
371        mammoData.InputVariables.Single(x => x.Value == "Shape"), true);
372      mammoData.InputVariables.SetItemCheckedState(
373        mammoData.InputVariables.Single(x => x.Value == "Margin"), true);
374      mammoData.InputVariables.SetItemCheckedState(
375        mammoData.InputVariables.Single(x => x.Value == "Density"), true);
376      mammoData.InputVariables.SetItemCheckedState(
377        mammoData.InputVariables.Single(x => x.Value == "Severity"), false);
378      mammoData.TrainingPartition.Start = 0;
379      mammoData.TrainingPartition.End = 800;
380      mammoData.TestPartition.Start = 800;
381      mammoData.TestPartition.End = 961;
382      mammoData.Name = "Data imported from mammographic_masses.csv";
383      mammoData.Description = "Original dataset: http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass, missing values have been replaced with median values.";
384      symbClassProblem.ProblemData = mammoData;
385
386      // configure grammar
387      var grammar = new TypeCoherentExpressionGrammar();
388      grammar.ConfigureAsDefaultClassificationGrammar();
389      grammar.Symbols.OfType<VariableCondition>().Single().Enabled = false;
390      var varSymbol = grammar.Symbols.OfType<Variable>().Where(x => !(x is LaggedVariable)).Single();
391      varSymbol.WeightMu = 1.0;
392      varSymbol.WeightSigma = 1.0;
393      varSymbol.WeightManipulatorMu = 0.0;
394      varSymbol.WeightManipulatorSigma = 0.05;
395      varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
396      var constSymbol = grammar.Symbols.OfType<Constant>().Single();
397      constSymbol.MaxValue = 20;
398      constSymbol.MinValue = -20;
399      constSymbol.ManipulatorMu = 0.0;
400      constSymbol.ManipulatorSigma = 1;
401      constSymbol.MultiplicativeManipulatorSigma = 0.03;
402      symbClassProblem.SymbolicExpressionTreeGrammar = grammar;
403
404      // configure remaining problem parameters
405      symbClassProblem.BestKnownQuality.Value = 0.0;
406      symbClassProblem.FitnessCalculationPartition.Start = 0;
407      symbClassProblem.FitnessCalculationPartition.End = 400;
408      symbClassProblem.ValidationPartition.Start = 400;
409      symbClassProblem.ValidationPartition.End = 800;
410      symbClassProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
411      symbClassProblem.MaximumSymbolicExpressionTreeLength.Value = 100;
412      symbClassProblem.MaximumSymbolicExpressionTreeDepth.Value = 10;
413      symbClassProblem.MaximumFunctionDefinitions.Value = 0;
414      symbClassProblem.MaximumFunctionArguments.Value = 0;
415      symbClassProblem.EvaluatorParameter.Value = new SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator();
416      #endregion
417      #region Algorithm Configuration
418      ga.Problem = symbClassProblem;
419      ga.Name = "Genetic Programming - Symbolic Classification";
420      ga.Description = "A standard genetic programming algorithm to solve a classification problem (Mammographic+Mass dataset)";
421      ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
422        ga, 1000, 1, 100, 0.15, 5
423        );
424
425      var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
426      mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
427      mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
428
429      ga.Analyzer.Operators.SetItemCheckedState(
430        ga.Analyzer.Operators
431        .OfType<SymbolicClassificationSingleObjectiveOverfittingAnalyzer>()
432        .Single(), false);
433      ga.Analyzer.Operators.SetItemCheckedState(
434        ga.Analyzer.Operators
435        .OfType<SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
436        .First(), false);
437      #endregion
438      return ga;
439    }
440    #endregion
441    #endregion
442
443    #region ES
444    #region Griewank
445    [TestMethod]
446    public void CreateEsGriewankSampleTest() {
447      var es = CreateEsGriewankSample();
448      XmlGenerator.Serialize(es, "../../ES_Griewank.hl");
449    }
450    [TestMethod]
451    public void RunEsGriewankSampleTest() {
452      var es = CreateEsGriewankSample();
453      es.SetSeedRandomly.Value = false;
454      RunAlgorithm(es);
455      Assert.AreEqual(0, GetDoubleResult(es, "BestQuality"));
456      Assert.AreEqual(0, GetDoubleResult(es, "CurrentAverageQuality"));
457      Assert.AreEqual(0, GetDoubleResult(es, "CurrentWorstQuality"));
458      Assert.AreEqual(100020, GetIntResult(es, "EvaluatedSolutions"));
459    }
460
461    private EvolutionStrategy CreateEsGriewankSample() {
462      EvolutionStrategy es = new EvolutionStrategy();
463      #region Problem Configuration
464      SingleObjectiveTestFunctionProblem problem = new SingleObjectiveTestFunctionProblem();
465
466      problem.ProblemSize.Value = 10;
467      problem.EvaluatorParameter.Value = new GriewankEvaluator();
468      problem.SolutionCreatorParameter.Value = new UniformRandomRealVectorCreator();
469      problem.Maximization.Value = false;
470      problem.Bounds = new DoubleMatrix(new double[,] { { -600, 600 } });
471      problem.BestKnownQuality.Value = 0;
472      problem.BestKnownSolutionParameter.Value = new RealVector(10);
473      problem.Name = "Single Objective Test Function";
474      problem.Description = "Test function with real valued inputs and a single objective.";
475      #endregion
476      #region Algorithm Configuration
477      es.Name = "Evolution Strategy - Griewank";
478      es.Description = "An evolution strategy which solves the 10-dimensional Griewank test function";
479      es.Problem = problem;
480      ConfigureEvolutionStrategyParameters<AverageCrossover, NormalAllPositionsManipulator,
481        StdDevStrategyVectorCreator, StdDevStrategyVectorCrossover, StdDevStrategyVectorManipulator>(
482        es, 20, 500, 2, 200, false);
483
484      StdDevStrategyVectorCreator strategyCreator = (StdDevStrategyVectorCreator)es.StrategyParameterCreator;
485      strategyCreator.BoundsParameter.Value = new DoubleMatrix(new double[,] { { 1, 20 } });
486
487      StdDevStrategyVectorManipulator strategyManipulator = (StdDevStrategyVectorManipulator)es.StrategyParameterManipulator;
488      strategyManipulator.BoundsParameter.Value = new DoubleMatrix(new double[,] { { 1E-12, 30 } });
489      strategyManipulator.GeneralLearningRateParameter.Value = new DoubleValue(0.22360679774997896);
490      strategyManipulator.LearningRateParameter.Value = new DoubleValue(0.39763536438352531);
491      #endregion
492      return es;
493    }
494    #endregion
495    #endregion
496
497    #region Island GA
498    #region TSP
499    [TestMethod]
500    public void CreateIslandGaTspSampleTest() {
501      var ga = CreateIslandGaTspSample();
502      XmlGenerator.Serialize(ga, "../../IslandGA_TSP.hl");
503    }
504    [TestMethod]
505    public void RunIslandGaTspSampleTest() {
506      var ga = CreateIslandGaTspSample();
507      ga.SetSeedRandomly.Value = false;
508      RunAlgorithm(ga);
509      Assert.AreEqual(9918, GetDoubleResult(ga, "BestQuality"));
510      Assert.AreEqual(10324.64, GetDoubleResult(ga, "CurrentAverageQuality"));
511      Assert.AreEqual(11823, GetDoubleResult(ga, "CurrentWorstQuality"));
512      Assert.AreEqual(495500, GetIntResult(ga, "EvaluatedSolutions"));
513    }
514
515    private IslandGeneticAlgorithm CreateIslandGaTspSample() {
516      IslandGeneticAlgorithm ga = new IslandGeneticAlgorithm();
517      #region Problem Configuration
518      var provider = new TSPLIBTSPInstanceProvider();
519      var instance = provider.GetDataDescriptors().Where(x => x.Name == "ch130").Single();
520      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
521      tspProblem.Load(provider.LoadData(instance));
522      tspProblem.UseDistanceMatrix.Value = true;
523      #endregion
524      #region Algorithm Configuration
525      ga.Name = "Island Genetic Algorithm - TSP";
526      ga.Description = "An island genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
527      ga.Problem = tspProblem;
528      ConfigureIslandGeneticAlgorithmParameters<ProportionalSelector, OrderCrossover2, InversionManipulator,
529        UnidirectionalRingMigrator, BestSelector, WorstReplacer>(
530        ga, 100, 1, 1000, 0.05, 5, 50, 0.25);
531
532      ga.Analyzer.Operators.SetItemCheckedState(ga.Analyzer.Operators
533        .OfType<TSPAlleleFrequencyAnalyzer>()
534        .Single(), false);
535      ga.Analyzer.Operators.SetItemCheckedState(ga.Analyzer.Operators
536        .OfType<TSPPopulationDiversityAnalyzer>()
537        .Single(), false);
538      #endregion
539      return ga;
540    }
541    #endregion
542    #endregion
543
544    #region LS
545    #region Knapsack
546    [TestMethod]
547    public void CreateLocalSearchKnapsackSampleTest() {
548      var ls = CreateLocalSearchKnapsackSample();
549      XmlGenerator.Serialize(ls, "../../LS_Knapsack.hl");
550    }
551    [TestMethod]
552    public void RunLocalSearchKnapsackSampleTest() {
553      var ls = CreateLocalSearchKnapsackSample();
554      ls.SetSeedRandomly.Value = false;
555      RunAlgorithm(ls);
556      Assert.AreEqual(345, GetDoubleResult(ls, "BestQuality"));
557      Assert.AreEqual(340.70731707317071, GetDoubleResult(ls, "CurrentAverageQuality"));
558      Assert.AreEqual(337, GetDoubleResult(ls, "CurrentWorstQuality"));
559      Assert.AreEqual(82000, GetIntResult(ls, "EvaluatedMoves"));
560    }
561
562    private LocalSearch CreateLocalSearchKnapsackSample() {
563      LocalSearch ls = new LocalSearch();
564      #region Problem Configuration
565      KnapsackProblem problem = new KnapsackProblem();
566      problem.BestKnownQuality = new DoubleValue(362);
567      problem.BestKnownSolution = new HeuristicLab.Encodings.BinaryVectorEncoding.BinaryVector(new bool[] {
568       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});
569      problem.EvaluatorParameter.Value = new KnapsackEvaluator();
570      problem.SolutionCreatorParameter.Value = new RandomBinaryVectorCreator();
571      problem.KnapsackCapacity.Value = 297;
572      problem.Maximization.Value = true;
573      problem.Penalty.Value = 1;
574      problem.Values = new IntArray(new int[] {
575  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});
576      problem.Weights = new IntArray(new int[] {
577 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});
578      problem.Name = "Knapsack Problem";
579      problem.Description = "Represents a Knapsack problem.";
580      #endregion
581      #region Algorithm Configuration
582      ls.Name = "Local Search - Knapsack";
583      ls.Description = "A local search algorithm that solves a randomly generated Knapsack problem";
584      ls.Problem = problem;
585      ls.MaximumIterations.Value = 1000;
586      ls.MoveEvaluator = ls.MoveEvaluatorParameter.ValidValues
587        .OfType<KnapsackOneBitflipMoveEvaluator>()
588        .Single();
589      ls.MoveGenerator = ls.MoveGeneratorParameter.ValidValues
590        .OfType<ExhaustiveOneBitflipMoveGenerator>()
591        .Single();
592      ls.MoveMaker = ls.MoveMakerParameter.ValidValues
593        .OfType<OneBitflipMoveMaker>()
594        .Single();
595      ls.SampleSize.Value = 100;
596      ls.Seed.Value = 0;
597      ls.SetSeedRandomly.Value = true;
598      #endregion
599      ls.Engine = new ParallelEngine();
600      return ls;
601    }
602    #endregion
603    #endregion
604
605    #region PSO
606    #region Schwefel
607    [TestMethod]
608    public void CreatePsoSchwefelSampleTest() {
609      var pso = CreatePsoSchwefelSample();
610      XmlGenerator.Serialize(pso, "../../PSO_Schwefel.hl");
611    }
612    [TestMethod]
613    public void RunPsoSchwefelSampleTest() {
614      var pso = CreatePsoSchwefelSample();
615      pso.SetSeedRandomly.Value = false;
616      RunAlgorithm(pso);
617      if (!Environment.Is64BitProcess) {
618        Assert.AreEqual(118.44027985932837, GetDoubleResult(pso, "BestQuality"));
619        Assert.AreEqual(140.71570105946438, GetDoubleResult(pso, "CurrentAverageQuality"));
620        Assert.AreEqual(220.956806502853, GetDoubleResult(pso, "CurrentWorstQuality"));
621        Assert.AreEqual(1000, GetIntResult(pso, "Iterations"));
622      } else {
623        Assert.AreEqual(118.43958282879345, GetDoubleResult(pso, "BestQuality"));
624        Assert.AreEqual(139.43946864779372, GetDoubleResult(pso, "CurrentAverageQuality"));
625        Assert.AreEqual(217.14654589055152, GetDoubleResult(pso, "CurrentWorstQuality"));
626        Assert.AreEqual(1000, GetIntResult(pso, "Iterations"));
627      }
628    }
629    private ParticleSwarmOptimization CreatePsoSchwefelSample() {
630      ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
631      #region Problem Configuration
632      var problem = new SingleObjectiveTestFunctionProblem();
633      problem.BestKnownQuality.Value = 0.0;
634      problem.BestKnownSolutionParameter.Value = new RealVector(new double[] { 420.968746, 420.968746 });
635      problem.Bounds = new DoubleMatrix(new double[,] { { -500, 500 } });
636      problem.EvaluatorParameter.Value = new SchwefelEvaluator();
637      problem.Maximization.Value = false;
638      problem.ProblemSize.Value = 2;
639      problem.SolutionCreatorParameter.Value = new UniformRandomRealVectorCreator();
640      #endregion
641      #region Algorithm Configuration
642      pso.Name = "Particle Swarm Optimization - Schwefel";
643      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)";
644      pso.Problem = problem;
645      pso.Inertia.Value = 10;
646      pso.MaxIterations.Value = 1000;
647      pso.NeighborBestAttraction.Value = 0.5;
648      pso.PersonalBestAttraction.Value = -0.01;
649      pso.SwarmSize.Value = 50;
650
651      var inertiaUpdater = pso.InertiaUpdaterParameter.ValidValues
652        .OfType<ExponentialDiscreteDoubleValueModifier>()
653        .Single();
654      inertiaUpdater.StartValueParameter.Value = new DoubleValue(10);
655      inertiaUpdater.EndValueParameter.Value = new DoubleValue(1);
656      pso.InertiaUpdater = inertiaUpdater;
657
658      pso.ParticleCreator = pso.ParticleCreatorParameter.ValidValues
659        .OfType<RealVectorParticleCreator>()
660        .Single();
661      var swarmUpdater = pso.SwarmUpdaterParameter.ValidValues
662        .OfType<RealVectorSwarmUpdater>()
663        .Single();
664      swarmUpdater.VelocityBoundsIndexParameter.ActualName = "Iterations";
665      swarmUpdater.VelocityBoundsParameter.Value = new DoubleMatrix(new double[,] { { -10, 10 } });
666      swarmUpdater.VelocityBoundsStartValueParameter.Value = new DoubleValue(10.0);
667      swarmUpdater.VelocityBoundsEndValueParameter.Value = new DoubleValue(1.0);
668      swarmUpdater.VelocityBoundsScalingOperatorParameter.Value = swarmUpdater.VelocityBoundsScalingOperatorParameter.ValidValues
669        .OfType<ExponentialDiscreteDoubleValueModifier>()
670        .Single();
671
672      pso.TopologyInitializer = null;
673      pso.TopologyUpdater = null;
674      pso.SwarmUpdater = swarmUpdater;
675      pso.Seed.Value = 0;
676      pso.SetSeedRandomly.Value = true;
677      #endregion
678      pso.Engine = new ParallelEngine();
679      return pso;
680    }
681    #endregion
682    #endregion
683
684    #region SA
685    #region Rastrigin
686    [TestMethod]
687    public void CreateSimulatedAnnealingRastriginSampleTest() {
688      var sa = CreateSimulatedAnnealingRastriginSample();
689      XmlGenerator.Serialize(sa, "../../SA_Rastrigin.hl");
690    }
691    [TestMethod]
692    public void RunSimulatedAnnealingRastriginSampleTest() {
693      var sa = CreateSimulatedAnnealingRastriginSample();
694      sa.SetSeedRandomly.Value = false;
695      RunAlgorithm(sa);
696      Assert.AreEqual(0.00014039606034543795, GetDoubleResult(sa, "BestQuality"));
697      Assert.AreEqual(5000, GetIntResult(sa, "EvaluatedMoves"));
698    }
699    private SimulatedAnnealing CreateSimulatedAnnealingRastriginSample() {
700      SimulatedAnnealing sa = new SimulatedAnnealing();
701      #region Problem Configuration
702      var problem = new SingleObjectiveTestFunctionProblem();
703      problem.BestKnownQuality.Value = 0.0;
704      problem.BestKnownSolutionParameter.Value = new RealVector(new double[] { 0, 0 });
705      problem.Bounds = new DoubleMatrix(new double[,] { { -5.12, 5.12 } });
706      problem.EvaluatorParameter.Value = new RastriginEvaluator();
707      problem.Maximization.Value = false;
708      problem.ProblemSize.Value = 2;
709      problem.SolutionCreatorParameter.Value = new UniformRandomRealVectorCreator();
710      #endregion
711      #region Algorithm Configuration
712      sa.Name = "Simulated Annealing - Rastrigin";
713      sa.Description = "A simulated annealing algorithm that solves the 2-dimensional Rastrigin test function";
714      sa.Problem = problem;
715      var annealingOperator = sa.AnnealingOperatorParameter.ValidValues
716        .OfType<ExponentialDiscreteDoubleValueModifier>()
717        .Single();
718      annealingOperator.StartIndexParameter.Value = new IntValue(0);
719      sa.AnnealingOperator = annealingOperator;
720
721      sa.EndTemperature.Value = 1E-6;
722      sa.InnerIterations.Value = 50;
723      sa.MaximumIterations.Value = 100;
724      var moveEvaluator = sa.MoveEvaluatorParameter.ValidValues
725        .OfType<RastriginAdditiveMoveEvaluator>()
726        .Single();
727      moveEvaluator.A.Value = 10;
728      sa.MoveEvaluator = moveEvaluator;
729
730      var moveGenerator = sa.MoveGeneratorParameter.ValidValues
731        .OfType<StochasticNormalMultiMoveGenerator>()
732        .Single();
733      moveGenerator.SigmaParameter.Value = new DoubleValue(1);
734      sa.MoveGenerator = moveGenerator;
735
736      sa.MoveMaker = sa.MoveMakerParameter.ValidValues
737        .OfType<AdditiveMoveMaker>()
738        .Single();
739
740      sa.Seed.Value = 0;
741      sa.SetSeedRandomly.Value = true;
742      sa.StartTemperature.Value = 1;
743      #endregion
744      sa.Engine = new ParallelEngine();
745      return sa;
746    }
747    #endregion
748    #endregion
749
750    #region TS
751    #region TSP
752    [TestMethod]
753    public void CreateTabuSearchTspSampleTest() {
754      var ts = CreateTabuSearchTspSample();
755      XmlGenerator.Serialize(ts, "../../TS_TSP.hl");
756    }
757    [TestMethod]
758    public void RunTabuSearchTspSampleTest() {
759      var ts = CreateTabuSearchTspSample();
760      ts.SetSeedRandomly.Value = false;
761      RunAlgorithm(ts);
762      Assert.AreEqual(6441, GetDoubleResult(ts, "BestQuality"));
763      Assert.AreEqual(7401.666666666667, GetDoubleResult(ts, "CurrentAverageQuality"));
764      Assert.AreEqual(8418, GetDoubleResult(ts, "CurrentWorstQuality"));
765      Assert.AreEqual(750000, GetIntResult(ts, "EvaluatedMoves"));
766    }
767
768    private TabuSearch CreateTabuSearchTspSample() {
769      TabuSearch ts = new TabuSearch();
770      #region Problem Configuration
771      var provider = new TSPLIBTSPInstanceProvider();
772      var instance = provider.GetDataDescriptors().Where(x => x.Name == "ch130").Single();
773      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
774      tspProblem.Load(provider.LoadData(instance));
775      tspProblem.UseDistanceMatrix.Value = true;
776      #endregion
777      #region Algorithm Configuration
778      ts.Name = "Tabu Search - TSP";
779      ts.Description = "A tabu search algorithm that solves the \"ch130\" TSP (imported from TSPLIB)";
780      ts.Problem = tspProblem;
781
782      ts.MaximumIterations.Value = 1000;
783      // move generator has to be set first
784      var moveGenerator = ts.MoveGeneratorParameter.ValidValues
785        .OfType<StochasticInversionMultiMoveGenerator>()
786        .Single();
787      ts.MoveGenerator = moveGenerator;
788      var moveEvaluator = ts.MoveEvaluatorParameter.ValidValues
789        .OfType<TSPInversionMoveRoundedEuclideanPathEvaluator>()
790        .Single();
791      ts.MoveEvaluator = moveEvaluator;
792      var moveMaker = ts.MoveMakerParameter.ValidValues
793        .OfType<InversionMoveMaker>()
794        .Single();
795      ts.MoveMaker = moveMaker;
796      ts.SampleSize.Value = 750;
797      ts.Seed.Value = 0;
798      ts.SetSeedRandomly.Value = true;
799
800      var tabuChecker = ts.TabuCheckerParameter.ValidValues
801        .OfType<InversionMoveSoftTabuCriterion>()
802        .Single();
803      tabuChecker.UseAspirationCriterion.Value = true;
804      ts.TabuChecker = tabuChecker;
805
806      var tabuMaker = ts.TabuMakerParameter.ValidValues
807        .OfType<InversionMoveTabuMaker>()
808        .Single();
809      ts.TabuMaker = tabuMaker;
810      ts.TabuTenure.Value = 60;
811
812      ts.Analyzer.Operators.SetItemCheckedState(ts.Analyzer.Operators
813        .OfType<TSPAlleleFrequencyAnalyzer>()
814        .Single(), false);
815      ts.Analyzer.Operators.SetItemCheckedState(ts.Analyzer.Operators
816        .OfType<TSPPopulationDiversityAnalyzer>()
817        .Single(), false);
818      #endregion
819      ts.Engine = new ParallelEngine();
820      return ts;
821    }
822    #endregion
823    #endregion
824
825    #region VNS
826    #region TSP
827    [TestMethod]
828    public void CreateVnsTspSampleTest() {
829      var vns = CreateVnsTspSample();
830      XmlGenerator.Serialize(vns, "../../VNS_TSP.hl");
831    }
832    [TestMethod]
833    public void RunVnsTspSampleTest() {
834      var vns = CreateVnsTspSample();
835      vns.SetSeedRandomly = false;
836      RunAlgorithm(vns);
837      Assert.AreEqual(867, GetDoubleResult(vns, "BestQuality"));
838      Assert.AreEqual(867, GetDoubleResult(vns, "CurrentAverageQuality"));
839      Assert.AreEqual(867, GetDoubleResult(vns, "CurrentWorstQuality"));
840      Assert.AreEqual(12975173, GetIntResult(vns, "EvaluatedSolutions"));
841    }
842
843    private VariableNeighborhoodSearch CreateVnsTspSample() {
844      VariableNeighborhoodSearch vns = new VariableNeighborhoodSearch();
845      #region Problem Configuration
846      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
847      tspProblem.BestKnownSolution = new Permutation(PermutationTypes.Absolute, new int[] {
848117, 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,
849      });
850      tspProblem.Coordinates = new DoubleMatrix(new double[,] {
851{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}
852      });
853      tspProblem.BestKnownQuality = new DoubleValue(867);
854
855      tspProblem.EvaluatorParameter.Value = new TSPRoundedEuclideanPathEvaluator();
856      tspProblem.SolutionCreatorParameter.Value = new RandomPermutationCreator();
857      tspProblem.UseDistanceMatrix.Value = true;
858      tspProblem.Name = "Funny TSP";
859      tspProblem.Description = "Represents a symmetric Traveling Salesman Problem.";
860      #endregion
861      #region Algorithm Configuration
862      vns.Name = "Variable Neighborhood Search - TSP";
863      vns.Description = "A variable neighborhood search algorithm which solves a funny TSP instance";
864      vns.Problem = tspProblem;
865
866      var localImprovement = vns.LocalImprovementParameter.ValidValues
867        .OfType<LocalSearchImprovementOperator>()
868        .Single();
869      // move generator has to be set first
870      localImprovement.MoveGenerator = localImprovement.MoveGeneratorParameter.ValidValues
871        .OfType<StochasticInversionMultiMoveGenerator>()
872        .Single();
873      localImprovement.MoveEvaluator = localImprovement.MoveEvaluatorParameter.ValidValues
874        .OfType<TSPInversionMoveRoundedEuclideanPathEvaluator>()
875        .Single();
876      localImprovement.MoveMaker = localImprovement.MoveMakerParameter.ValidValues
877        .OfType<InversionMoveMaker>()
878        .Single();
879      localImprovement.SampleSizeParameter.Value = new IntValue(500);
880      vns.LocalImprovement = localImprovement;
881
882      vns.LocalImprovementMaximumIterations = 150;
883      vns.MaximumIterations = 25;
884      vns.Seed = 0;
885      vns.SetSeedRandomly = true;
886      var shakingOperator = vns.ShakingOperatorParameter.ValidValues
887        .OfType<PermutationShakingOperator>()
888        .Single();
889      shakingOperator.Operators.SetItemCheckedState(shakingOperator.Operators
890        .OfType<Swap2Manipulator>()
891        .Single(), false);
892      shakingOperator.Operators.SetItemCheckedState(shakingOperator.Operators
893        .OfType<Swap3Manipulator>()
894        .Single(), false);
895      vns.ShakingOperator = shakingOperator;
896      vns.Analyzer.Operators.SetItemCheckedState(vns.Analyzer.Operators
897        .OfType<TSPAlleleFrequencyAnalyzer>()
898        .Single(), false);
899      vns.Analyzer.Operators.SetItemCheckedState(vns.Analyzer.Operators
900        .OfType<TSPPopulationDiversityAnalyzer>()
901        .Single(), false);
902      #endregion
903      vns.Engine = new ParallelEngine();
904      return vns;
905    }
906    #endregion
907    #endregion
908
909    #region Helpers
910    private void ConfigureEvolutionStrategyParameters<R, M, SC, SR, SM>(EvolutionStrategy es, int popSize, int children, int parentsPerChild, int maxGens, bool plusSelection)
911      where R : ICrossover
912      where M : IManipulator
913      where SC : IStrategyParameterCreator
914      where SR : IStrategyParameterCrossover
915      where SM : IStrategyParameterManipulator {
916      es.PopulationSize.Value = popSize;
917      es.Children.Value = children;
918      es.ParentsPerChild.Value = parentsPerChild;
919      es.MaximumGenerations.Value = maxGens;
920      es.PlusSelection.Value = false;
921
922      es.Seed.Value = 0;
923      es.SetSeedRandomly.Value = true;
924
925      es.Recombinator = es.RecombinatorParameter.ValidValues
926        .OfType<R>()
927        .Single();
928
929      es.Mutator = es.MutatorParameter.ValidValues
930        .OfType<M>()
931        .Single();
932
933      es.StrategyParameterCreator = es.StrategyParameterCreatorParameter.ValidValues
934        .OfType<SC>()
935        .Single();
936      es.StrategyParameterCrossover = es.StrategyParameterCrossoverParameter.ValidValues
937        .OfType<SR>()
938        .Single();
939      es.StrategyParameterManipulator = es.StrategyParameterManipulatorParameter.ValidValues
940        .OfType<SM>()
941        .Single();
942      es.Engine = new ParallelEngine();
943    }
944
945    private void ConfigureGeneticAlgorithmParameters<S, C, M>(GeneticAlgorithm ga, int popSize, int elites, int maxGens, double mutationRate, int tournGroupSize = 0)
946      where S : ISelector
947      where C : ICrossover
948      where M : IManipulator {
949      ga.Elites.Value = elites;
950      ga.MaximumGenerations.Value = maxGens;
951      ga.MutationProbability.Value = mutationRate;
952      ga.PopulationSize.Value = popSize;
953      ga.Seed.Value = 0;
954      ga.SetSeedRandomly.Value = true;
955      ga.Selector = ga.SelectorParameter.ValidValues
956        .OfType<S>()
957        .First();
958
959      ga.Crossover = ga.CrossoverParameter.ValidValues
960        .OfType<C>()
961        .First();
962
963      ga.Mutator = ga.MutatorParameter.ValidValues
964        .OfType<M>()
965        .First();
966
967      var tSelector = ga.Selector as TournamentSelector;
968      if (tSelector != null) {
969        tSelector.GroupSizeParameter.Value.Value = tournGroupSize;
970      }
971      ga.Engine = new ParallelEngine();
972    }
973
974    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)
975      where S : ISelector
976      where C : ICrossover
977      where M : IManipulator
978      where Mi : IMigrator
979      where MiS : ISelector
980      where MiR : IReplacer {
981      ga.Elites.Value = elites;
982      ga.MaximumGenerations.Value = maxGens;
983      ga.MutationProbability.Value = mutationRate;
984      ga.PopulationSize.Value = popSize;
985      ga.NumberOfIslands.Value = numberOfIslands;
986      ga.MigrationInterval.Value = migrationInterval;
987      ga.MigrationRate.Value = migrationRate;
988      ga.Seed.Value = 0;
989      ga.SetSeedRandomly.Value = true;
990      ga.Selector = ga.SelectorParameter.ValidValues
991        .OfType<S>()
992        .Single();
993
994      ga.Crossover = ga.CrossoverParameter.ValidValues
995        .OfType<C>()
996        .Single();
997
998      ga.Mutator = ga.MutatorParameter.ValidValues
999        .OfType<M>()
1000        .Single();
1001      ga.Migrator = ga.MigratorParameter.ValidValues
1002        .OfType<Mi>()
1003        .Single();
1004      ga.EmigrantsSelector = ga.EmigrantsSelectorParameter.ValidValues
1005        .OfType<MiS>()
1006        .Single();
1007      ga.ImmigrationReplacer = ga.ImmigrationReplacerParameter.ValidValues
1008        .OfType<MiR>()
1009        .Single();
1010      ga.Engine = new ParallelEngine();
1011    }
1012
1013
1014    private void RunAlgorithm(IAlgorithm a) {
1015      var trigger = new EventWaitHandle(false, EventResetMode.ManualReset);
1016      Exception ex = null;
1017      a.Stopped += (src, e) => { trigger.Set(); };
1018      a.ExceptionOccurred += (src, e) => { ex = e.Value; trigger.Set(); };
1019      a.Prepare();
1020      a.Start();
1021      trigger.WaitOne();
1022
1023      Assert.AreEqual(ex, null);
1024    }
1025
1026    private double GetDoubleResult(IAlgorithm a, string resultName) {
1027      return ((DoubleValue)a.Results[resultName].Value).Value;
1028    }
1029    private int GetIntResult(IAlgorithm a, string resultName) {
1030      return ((IntValue)a.Results[resultName].Value).Value;
1031    }
1032    #endregion
1033  }
1034}
Note: See TracBrowser for help on using the repository browser.