Free cookie consent management tool by TermsFeed Policy Generator

source: trunk/sources/HeuristicLab.Tests/HeuristicLab-3.3/SamplesTest.cs @ 6928

Last change on this file since 6928 was 6928, checked in by mkommend, 13 years ago

#1653: Updated artifical ant unit test.
File size: 56.3 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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.Knapsack;
47using HeuristicLab.Problems.TestFunctions;
48using HeuristicLab.Problems.TravelingSalesman;
49using HeuristicLab.Problems.VehicleRouting;
50using HeuristicLab.Problems.VehicleRouting.Encodings.General;
51using HeuristicLab.Problems.VehicleRouting.Encodings.Potvin;
52using HeuristicLab.Selection;
53using Microsoft.VisualStudio.TestTools.UnitTesting;
54
55namespace HeuristicLab_33.Tests {
56  [TestClass]
57  [DeploymentItem(@"HeuristicLab-3.3/Resources/C101.opt.txt")]
58  [DeploymentItem(@"HeuristicLab-3.3/Resources/ch130.tsp")]
59  [DeploymentItem(@"HeuristicLab-3.3/Resources/ch130.opt.tour")]
60  [DeploymentItem(@"HeuristicLab-3.3/Resources/mammographic_masses.txt")]
61  [DeploymentItem(@"HeuristicLab-3.3/Resources/towerData.txt")]
62  [DeploymentItem(@"HeuristicLab-3.3/Resources/C101.txt")]
63  public class SamplesTest {
64    #region GA
65    #region TSP
66    [TestMethod]
67    public void CreateGaTspSampleTest() {
68      var ga = CreateGaTspSample();
69      XmlGenerator.Serialize(ga, "../../GA_TSP.hl");
70    }
71    [TestMethod]
72    public void RunGaTspSampleTest() {
73      var ga = CreateGaTspSample();
74      ga.SetSeedRandomly.Value = false;
75      RunAlgorithm(ga);
76      Assert.AreEqual(12332, GetDoubleResult(ga, "BestQuality"));
77      Assert.AreEqual(13123.2, GetDoubleResult(ga, "CurrentAverageQuality"));
78      Assert.AreEqual(14538, GetDoubleResult(ga, "CurrentWorstQuality"));
79      Assert.AreEqual(99100, GetIntResult(ga, "EvaluatedSolutions"));
80    }
81
82    private GeneticAlgorithm CreateGaTspSample() {
83      GeneticAlgorithm ga = new GeneticAlgorithm();
84      #region Problem Configuration
85      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
86      tspProblem.ImportFromTSPLIB("ch130.tsp", "ch130.opt.tour", 6110);
87      tspProblem.Evaluator = new TSPRoundedEuclideanPathEvaluator();
88      tspProblem.SolutionCreator = new RandomPermutationCreator();
89      tspProblem.UseDistanceMatrix.Value = true;
90      tspProblem.Name = "ch130 TSP (imported from TSPLIB)";
91      tspProblem.Description = "130 city problem (Churritz)";
92      #endregion
93      #region Algorithm Configuration
94      ga.Name = "Genetic Algorithm - TSP";
95      ga.Description = "A genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
96      ga.Problem = tspProblem;
97      ConfigureGeneticAlgorithmParameters<ProportionalSelector, OrderCrossover2, InversionManipulator>(
98        ga, 100, 1, 1000, 0.05);
99
100      ga.Analyzer.Operators.SetItemCheckedState(ga.Analyzer.Operators
101        .OfType<TSPAlleleFrequencyAnalyzer>()
102        .Single(), false);
103      ga.Analyzer.Operators.SetItemCheckedState(ga.Analyzer.Operators
104        .OfType<TSPPopulationDiversityAnalyzer>()
105        .Single(), false);
106      #endregion
107      return ga;
108    }
109    #endregion
110    #region VRP
111    [TestMethod]
112    public void CreateGaVrpSampleTest() {
113      var ga = CreateGaVrpSample();
114      XmlGenerator.Serialize(ga, "../../GA_VRP.hl");
115    }
116
117    [TestMethod]
118    public void RunGaVrpSampleTest() {
119      var ga = CreateGaVrpSample();
120      ga.SetSeedRandomly.Value = false;
121      RunAlgorithm(ga);
122      Assert.AreEqual(1828.9368669428336, GetDoubleResult(ga, "BestQuality"));
123      Assert.AreEqual(1831.5504074358635, GetDoubleResult(ga, "CurrentAverageQuality"));
124      Assert.AreEqual(1895.8980772167054, GetDoubleResult(ga, "CurrentWorstQuality"));
125      Assert.AreEqual(99100, GetIntResult(ga, "EvaluatedSolutions"));
126    }
127
128    private GeneticAlgorithm CreateGaVrpSample() {
129      GeneticAlgorithm ga = new GeneticAlgorithm();
130      #region Problem Configuration
131      VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
132
133      vrpProblem.ImportFromSolomon("C101.txt");
134      vrpProblem.ImportSolution("C101.opt.txt");
135      vrpProblem.Name = "C101 VRP (imported from Solomon)";
136      vrpProblem.Description = "Represents a Vehicle Routing Problem.";
137      vrpProblem.DistanceFactorParameter.Value.Value = 1;
138      vrpProblem.FleetUsageFactorParameter.Value.Value = 100;
139      vrpProblem.OverloadPenaltyParameter.Value.Value = 100;
140      vrpProblem.TardinessPenaltyParameter.Value.Value = 100;
141      vrpProblem.TimeFactorParameter.Value.Value = 0;
142      vrpProblem.Evaluator = new VRPEvaluator();
143      vrpProblem.MaximizationParameter.Value.Value = false;
144      vrpProblem.SolutionCreator = new RandomCreator();
145      vrpProblem.UseDistanceMatrix.Value = true;
146      vrpProblem.Vehicles.Value = 25;
147      #endregion
148      #region Algorithm Configuration
149      ga.Name = "Genetic Algorithm - VRP";
150      ga.Description = "A genetic algorithm which solves the \"C101\" vehicle routing problem (imported from Solomon)";
151      ga.Problem = vrpProblem;
152      ConfigureGeneticAlgorithmParameters<TournamentSelector, MultiVRPSolutionCrossover, MultiVRPSolutionManipulator>(
153        ga, 100, 1, 1000, 0.05, 3);
154
155      var xOver = (MultiVRPSolutionCrossover)ga.Crossover;
156      foreach (var op in xOver.Operators) {
157        xOver.Operators.SetItemCheckedState(op, false);
158      }
159      xOver.Operators.SetItemCheckedState(xOver.Operators
160        .OfType<PotvinRouteBasedCrossover>()
161        .Single(), true);
162      xOver.Operators.SetItemCheckedState(xOver.Operators
163        .OfType<PotvinSequenceBasedCrossover>()
164        .Single(), true);
165
166      var manipulator = (MultiVRPSolutionManipulator)ga.Mutator;
167      foreach (var op in manipulator.Operators) {
168        manipulator.Operators.SetItemCheckedState(op, false);
169      }
170      manipulator.Operators.SetItemCheckedState(manipulator.Operators
171        .OfType<PotvinOneLevelExchangeMainpulator>()
172        .Single(), true);
173      manipulator.Operators.SetItemCheckedState(manipulator.Operators
174        .OfType<PotvinTwoLevelExchangeManipulator>()
175        .Single(), true);
176      #endregion
177      return ga;
178    }
179    #endregion
180    #region ArtificialAnt
181    [TestMethod]
182    public void CreateGpArtificialAntSampleTest() {
183      var ga = CreateGpArtificialAntSample();
184      XmlGenerator.Serialize(ga, "../../SGP_SantaFe.hl");
185    }
186
187    [TestMethod]
188    public void RunGpArtificialAntSampleTest() {
189      var ga = CreateGpArtificialAntSample();
190      ga.SetSeedRandomly.Value = false;
191      RunAlgorithm(ga);
192      Assert.AreEqual(63, GetDoubleResult(ga, "BestQuality"));
193      Assert.AreEqual(49.59, GetDoubleResult(ga, "CurrentAverageQuality"));
194      Assert.AreEqual(0, GetDoubleResult(ga, "CurrentWorstQuality"));
195      Assert.AreEqual(50950, GetIntResult(ga, "EvaluatedSolutions"));
196    }
197
198    public GeneticAlgorithm CreateGpArtificialAntSample() {
199      GeneticAlgorithm ga = new GeneticAlgorithm();
200      #region Problem Configuration
201      ArtificialAntProblem antProblem = new ArtificialAntProblem();
202      antProblem.BestKnownQuality.Value = 89;
203      antProblem.MaxExpressionDepth.Value = 10;
204      antProblem.MaxExpressionLength.Value = 100;
205      antProblem.MaxFunctionArguments.Value = 3;
206      antProblem.MaxFunctionDefinitions.Value = 3;
207      antProblem.MaxTimeSteps.Value = 600;
208      #endregion
209      #region Algorithm Configuration
210      ga.Name = "Genetic Programming - Artificial Ant";
211      ga.Description = "A standard genetic programming algorithm to solve the artificial ant problem (Santa-Fe trail)";
212      ga.Problem = antProblem;
213      ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeArchitectureManipulator>(
214        ga, 1000, 1, 50, 0.15, 5);
215      var mutator = (MultiSymbolicExpressionTreeArchitectureManipulator)ga.Mutator;
216      mutator.Operators.SetItemCheckedState(mutator.Operators
217        .OfType<FullTreeShaker>()
218        .Single(), false);
219      mutator.Operators.SetItemCheckedState(mutator.Operators
220        .OfType<OnePointShaker>()
221        .Single(), false);
222      mutator.Operators.SetItemCheckedState(mutator.Operators
223        .OfType<ArgumentDeleter>()
224        .Single(), false);
225      mutator.Operators.SetItemCheckedState(mutator.Operators
226        .OfType<SubroutineDeleter>()
227        .Single(), false);
228      #endregion
229      return ga;
230    }
231    #endregion
232    #region Symbolic Regression
233    [TestMethod]
234    public void CreateGpSymbolicRegressionSampleTest() {
235      var ga = CreateGpSymbolicRegressionSample();
236      XmlGenerator.Serialize(ga, "../../SGP_SymbReg.hl");
237    }
238    [TestMethod]
239    public void RunGpSymbolicRegressionSampleTest() {
240      var ga = CreateGpSymbolicRegressionSample();
241      ga.SetSeedRandomly.Value = false;
242      RunAlgorithm(ga);
243      Assert.AreEqual(0.82345620379844975, GetDoubleResult(ga, "BestQuality"));
244      Assert.AreEqual(0.57227221137175377, GetDoubleResult(ga, "CurrentAverageQuality"));
245      Assert.AreEqual(0, GetDoubleResult(ga, "CurrentWorstQuality"));
246      Assert.AreEqual(50950, GetIntResult(ga, "EvaluatedSolutions"));
247    }
248
249    private GeneticAlgorithm CreateGpSymbolicRegressionSample() {
250      GeneticAlgorithm ga = new GeneticAlgorithm();
251      #region Problem Configuration
252      SymbolicRegressionSingleObjectiveProblem symbRegProblem = new SymbolicRegressionSingleObjectiveProblem();
253      symbRegProblem.Name = "Tower Symbolic Regression Problem";
254      symbRegProblem.Description = "Tower Dataset (downloaded from: http://vanillamodeling.com/realproblems.html)";
255      var towerProblemData = RegressionProblemData.ImportFromFile("towerData.txt");
256      towerProblemData.TargetVariableParameter.Value = towerProblemData.TargetVariableParameter.ValidValues
257        .First(v => v.Value == "towerResponse");
258      towerProblemData.InputVariables.SetItemCheckedState(
259        towerProblemData.InputVariables.Single(x => x.Value == "x1"), true);
260      towerProblemData.InputVariables.SetItemCheckedState(
261        towerProblemData.InputVariables.Single(x => x.Value == "x7"), false);
262      towerProblemData.InputVariables.SetItemCheckedState(
263        towerProblemData.InputVariables.Single(x => x.Value == "x11"), false);
264      towerProblemData.InputVariables.SetItemCheckedState(
265        towerProblemData.InputVariables.Single(x => x.Value == "x16"), false);
266      towerProblemData.InputVariables.SetItemCheckedState(
267        towerProblemData.InputVariables.Single(x => x.Value == "x21"), false);
268      towerProblemData.InputVariables.SetItemCheckedState(
269        towerProblemData.InputVariables.Single(x => x.Value == "x25"), false);
270      towerProblemData.InputVariables.SetItemCheckedState(
271        towerProblemData.InputVariables.Single(x => x.Value == "towerResponse"), false);
272      towerProblemData.TrainingPartition.Start = 0;
273      towerProblemData.TrainingPartition.End = 4000;
274      towerProblemData.TestPartition.Start = 4000;
275      towerProblemData.TestPartition.End = 4999;
276      towerProblemData.Name = "Data imported from towerData.txt";
277      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";
278      symbRegProblem.ProblemData = towerProblemData;
279
280      // configure grammar
281      var grammar = new TypeCoherentExpressionGrammar();
282      grammar.Symbols.OfType<Sine>().Single().InitialFrequency = 0.0;
283      grammar.Symbols.OfType<Cosine>().Single().InitialFrequency = 0.0;
284      grammar.Symbols.OfType<Tangent>().Single().InitialFrequency = 0.0;
285      grammar.Symbols.OfType<IfThenElse>().Single().InitialFrequency = 0.0;
286      grammar.Symbols.OfType<GreaterThan>().Single().InitialFrequency = 0.0;
287      grammar.Symbols.OfType<LessThan>().Single().InitialFrequency = 0.0;
288      grammar.Symbols.OfType<And>().Single().InitialFrequency = 0.0;
289      grammar.Symbols.OfType<Or>().Single().InitialFrequency = 0.0;
290      grammar.Symbols.OfType<Not>().Single().InitialFrequency = 0.0;
291      grammar.Symbols.OfType<TimeLag>().Single().InitialFrequency = 0.0;
292      grammar.Symbols.OfType<Integral>().Single().InitialFrequency = 0.0;
293      grammar.Symbols.OfType<Derivative>().Single().InitialFrequency = 0.0;
294      grammar.Symbols.OfType<LaggedVariable>().Single().InitialFrequency = 0.0;
295      grammar.Symbols.OfType<VariableCondition>().Single().InitialFrequency = 0.0;
296      var varSymbol = grammar.Symbols.OfType<Variable>().Where(x => !(x is LaggedVariable)).Single();
297      varSymbol.WeightMu = 1.0;
298      varSymbol.WeightSigma = 1.0;
299      varSymbol.WeightManipulatorMu = 0.0;
300      varSymbol.WeightManipulatorSigma = 0.05;
301      varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
302      var constSymbol = grammar.Symbols.OfType<Constant>().Single();
303      constSymbol.MaxValue = 20;
304      constSymbol.MinValue = -20;
305      constSymbol.ManipulatorMu = 0.0;
306      constSymbol.ManipulatorSigma = 1;
307      constSymbol.MultiplicativeManipulatorSigma = 0.03;
308      symbRegProblem.SymbolicExpressionTreeGrammar = grammar;
309
310      // configure remaining problem parameters
311      symbRegProblem.BestKnownQuality.Value = 0.97;
312      symbRegProblem.FitnessCalculationPartition.Start = 0;
313      symbRegProblem.FitnessCalculationPartition.End = 2800;
314      symbRegProblem.ValidationPartition.Start = 2800;
315      symbRegProblem.ValidationPartition.End = 4000;
316      symbRegProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
317      symbRegProblem.MaximumSymbolicExpressionTreeLength.Value = 150;
318      symbRegProblem.MaximumSymbolicExpressionTreeDepth.Value = 12;
319      symbRegProblem.MaximumFunctionDefinitions.Value = 0;
320      symbRegProblem.MaximumFunctionArguments.Value = 0;
321
322      symbRegProblem.EvaluatorParameter.Value = new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator();
323      #endregion
324      #region Algorithm Configuration
325      ga.Problem = symbRegProblem;
326      ga.Name = "Genetic Programming - Symbolic Regression";
327      ga.Description = "A standard genetic programming algorithm to solve a symbolic regression problem (tower dataset)";
328      ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
329        ga, 1000, 1, 50, 0.15, 5);
330      var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
331      mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
332      mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
333
334      ga.Analyzer.Operators.SetItemCheckedState(
335        ga.Analyzer.Operators
336        .OfType<SymbolicRegressionSingleObjectiveOverfittingAnalyzer>()
337        .Single(), false);
338      ga.Analyzer.Operators.SetItemCheckedState(
339        ga.Analyzer.Operators
340        .OfType<SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
341        .First(), false);
342      #endregion
343      return ga;
344    }
345    #endregion
346    #region Symbolic Classification
347    [TestMethod]
348    public void CreateGpSymbolicClassificationSampleTest() {
349      var ga = CreateGpSymbolicClassificationSample();
350      XmlGenerator.Serialize(ga, "../../SGP_SymbClass.hl");
351    }
352
353    [TestMethod]
354    public void RunGpSymbolicClassificationSampleTest() {
355      var ga = CreateGpSymbolicClassificationSample();
356      ga.SetSeedRandomly.Value = false;
357      RunAlgorithm(ga);
358      Assert.AreEqual(0.14846929004393383, GetDoubleResult(ga, "BestQuality"));
359      Assert.AreEqual(2.4746939351100066, GetDoubleResult(ga, "CurrentAverageQuality"));
360      Assert.AreEqual(100.43641619798549, GetDoubleResult(ga, "CurrentWorstQuality"));
361      Assert.AreEqual(100900, GetIntResult(ga, "EvaluatedSolutions"));
362    }
363
364    private GeneticAlgorithm CreateGpSymbolicClassificationSample() {
365      GeneticAlgorithm ga = new GeneticAlgorithm();
366      #region Problem Configuration
367      SymbolicClassificationSingleObjectiveProblem symbClassProblem = new SymbolicClassificationSingleObjectiveProblem();
368      symbClassProblem.Name = "Mammography Classification Problem";
369      symbClassProblem.Description = "Mammography dataset imported from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass)";
370      var mammoData = ClassificationProblemData.ImportFromFile("mammographic_masses.txt");
371      mammoData.TargetVariableParameter.Value = mammoData.TargetVariableParameter.ValidValues
372        .First(v => v.Value == "Severity");
373      mammoData.InputVariables.SetItemCheckedState(
374        mammoData.InputVariables.Single(x => x.Value == "BI-RADS"), false);
375      mammoData.InputVariables.SetItemCheckedState(
376        mammoData.InputVariables.Single(x => x.Value == "Age"), true);
377      mammoData.InputVariables.SetItemCheckedState(
378        mammoData.InputVariables.Single(x => x.Value == "Shape"), true);
379      mammoData.InputVariables.SetItemCheckedState(
380        mammoData.InputVariables.Single(x => x.Value == "Margin"), true);
381      mammoData.InputVariables.SetItemCheckedState(
382        mammoData.InputVariables.Single(x => x.Value == "Density"), true);
383      mammoData.InputVariables.SetItemCheckedState(
384        mammoData.InputVariables.Single(x => x.Value == "Severity"), false);
385      mammoData.TrainingPartition.Start = 0;
386      mammoData.TrainingPartition.End = 800;
387      mammoData.TestPartition.Start = 800;
388      mammoData.TestPartition.End = 961;
389      mammoData.Name = "Data imported from mammographic_masses.csv";
390      mammoData.Description = "Original dataset: http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass, missing values have been replaced with median values.";
391      symbClassProblem.ProblemData = mammoData;
392
393      // configure grammar
394      var grammar = new TypeCoherentExpressionGrammar();
395      grammar.Symbols.OfType<Sine>().Single().InitialFrequency = 0.0;
396      grammar.Symbols.OfType<Cosine>().Single().InitialFrequency = 0.0;
397      grammar.Symbols.OfType<Tangent>().Single().InitialFrequency = 0.0;
398      grammar.Symbols.OfType<Power>().Single().InitialFrequency = 0.0;
399      grammar.Symbols.OfType<Root>().Single().InitialFrequency = 0.0;
400      grammar.Symbols.OfType<TimeLag>().Single().InitialFrequency = 0.0;
401      grammar.Symbols.OfType<Integral>().Single().InitialFrequency = 0.0;
402      grammar.Symbols.OfType<Derivative>().Single().InitialFrequency = 0.0;
403      grammar.Symbols.OfType<LaggedVariable>().Single().InitialFrequency = 0.0;
404      grammar.Symbols.OfType<VariableCondition>().Single().InitialFrequency = 0.0;
405      var varSymbol = grammar.Symbols.OfType<Variable>().Where(x => !(x is LaggedVariable)).Single();
406      varSymbol.WeightMu = 1.0;
407      varSymbol.WeightSigma = 1.0;
408      varSymbol.WeightManipulatorMu = 0.0;
409      varSymbol.WeightManipulatorSigma = 0.05;
410      varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
411      var constSymbol = grammar.Symbols.OfType<Constant>().Single();
412      constSymbol.MaxValue = 20;
413      constSymbol.MinValue = -20;
414      constSymbol.ManipulatorMu = 0.0;
415      constSymbol.ManipulatorSigma = 1;
416      constSymbol.MultiplicativeManipulatorSigma = 0.03;
417      symbClassProblem.SymbolicExpressionTreeGrammar = grammar;
418
419      // configure remaining problem parameters
420      symbClassProblem.BestKnownQuality.Value = 0.0;
421      symbClassProblem.FitnessCalculationPartition.Start = 0;
422      symbClassProblem.FitnessCalculationPartition.End = 400;
423      symbClassProblem.ValidationPartition.Start = 400;
424      symbClassProblem.ValidationPartition.End = 800;
425      symbClassProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
426      symbClassProblem.MaximumSymbolicExpressionTreeLength.Value = 100;
427      symbClassProblem.MaximumSymbolicExpressionTreeDepth.Value = 10;
428      symbClassProblem.MaximumFunctionDefinitions.Value = 0;
429      symbClassProblem.MaximumFunctionArguments.Value = 0;
430      symbClassProblem.EvaluatorParameter.Value = new SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator();
431      #endregion
432      #region Algorithm Configuration
433      ga.Problem = symbClassProblem;
434      ga.Name = "Genetic Programming - Symbolic Classification";
435      ga.Description = "A standard genetic programming algorithm to solve a classification problem (Mammographic+Mass dataset)";
436      ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
437        ga, 1000, 1, 100, 0.15, 5
438        );
439
440      var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
441      mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
442      mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
443
444      ga.Analyzer.Operators.SetItemCheckedState(
445        ga.Analyzer.Operators
446        .OfType<SymbolicClassificationSingleObjectiveOverfittingAnalyzer>()
447        .Single(), false);
448      ga.Analyzer.Operators.SetItemCheckedState(
449        ga.Analyzer.Operators
450        .OfType<SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
451        .First(), false);
452      #endregion
453      return ga;
454    }
455    #endregion
456    #endregion
457
458    #region ES
459    #region Griewank
460    [TestMethod]
461    public void CreateEsGriewankSampleTest() {
462      var es = CreateEsGriewankSample();
463      XmlGenerator.Serialize(es, "../../ES_Griewank.hl");
464    }
465    [TestMethod]
466    public void RunEsGriewankSampleTest() {
467      var es = CreateEsGriewankSample();
468      es.SetSeedRandomly.Value = false;
469      RunAlgorithm(es);
470      Assert.AreEqual(0, GetDoubleResult(es, "BestQuality"));
471      Assert.AreEqual(0, GetDoubleResult(es, "CurrentAverageQuality"));
472      Assert.AreEqual(0, GetDoubleResult(es, "CurrentWorstQuality"));
473      Assert.AreEqual(100020, GetIntResult(es, "EvaluatedSolutions"));
474    }
475
476    private EvolutionStrategy CreateEsGriewankSample() {
477      EvolutionStrategy es = new EvolutionStrategy();
478      #region Problem Configuration
479      SingleObjectiveTestFunctionProblem problem = new SingleObjectiveTestFunctionProblem();
480
481      problem.ProblemSize.Value = 10;
482      problem.Evaluator = new GriewankEvaluator();
483      problem.SolutionCreator = new UniformRandomRealVectorCreator();
484      problem.Maximization.Value = false;
485      problem.Bounds = new DoubleMatrix(new double[,] { { -600, 600 } });
486      problem.BestKnownQuality.Value = 0;
487      problem.BestKnownSolutionParameter.Value = new RealVector(10);
488      problem.Name = "Single Objective Test Function";
489      problem.Description = "Test function with real valued inputs and a single objective.";
490      #endregion
491      #region Algorithm Configuration
492      es.Name = "Evolution Strategy - Griewank";
493      es.Description = "An evolution strategy which solves the 10-dimensional Griewank test function";
494      es.Problem = problem;
495      ConfigureEvolutionStrategyParameters<AverageCrossover, NormalAllPositionsManipulator,
496        StdDevStrategyVectorCreator, StdDevStrategyVectorCrossover, StdDevStrategyVectorManipulator>(
497        es, 20, 500, 2, 200, false);
498
499      StdDevStrategyVectorCreator strategyCreator = (StdDevStrategyVectorCreator)es.StrategyParameterCreator;
500      strategyCreator.BoundsParameter.Value = new DoubleMatrix(new double[,] { { 1, 20 } });
501
502      StdDevStrategyVectorManipulator strategyManipulator = (StdDevStrategyVectorManipulator)es.StrategyParameterManipulator;
503      strategyManipulator.BoundsParameter.Value = new DoubleMatrix(new double[,] { { 1E-12, 30 } });
504      strategyManipulator.GeneralLearningRateParameter.Value = new DoubleValue(0.22360679774997896);
505      strategyManipulator.LearningRateParameter.Value = new DoubleValue(0.39763536438352531);
506      #endregion
507      return es;
508    }
509    #endregion
510    #endregion
511
512    #region Island GA
513    #region TSP
514    [TestMethod]
515    public void CreateIslandGaTspSampleTest() {
516      var ga = CreateIslandGaTspSample();
517      XmlGenerator.Serialize(ga, "../../IslandGA_TSP.hl");
518    }
519    [TestMethod]
520    public void RunIslandGaTspSampleTest() {
521      var ga = CreateIslandGaTspSample();
522      ga.SetSeedRandomly.Value = false;
523      RunAlgorithm(ga);
524      Assert.AreEqual(10469, GetDoubleResult(ga, "BestQuality"));
525      Assert.AreEqual(11184.87, GetDoubleResult(ga, "CurrentAverageQuality"));
526      Assert.AreEqual(13420, GetDoubleResult(ga, "CurrentWorstQuality"));
527      Assert.AreEqual(495500, GetIntResult(ga, "EvaluatedSolutions"));
528    }
529
530    private IslandGeneticAlgorithm CreateIslandGaTspSample() {
531      IslandGeneticAlgorithm ga = new IslandGeneticAlgorithm();
532      #region Problem Configuration
533      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
534      tspProblem.ImportFromTSPLIB("ch130.tsp", "ch130.opt.tour", 6110);
535      tspProblem.Evaluator = new TSPRoundedEuclideanPathEvaluator();
536      tspProblem.SolutionCreator = new RandomPermutationCreator();
537      tspProblem.UseDistanceMatrix.Value = true;
538      tspProblem.Name = "ch130 TSP (imported from TSPLIB)";
539      tspProblem.Description = "130 city problem (Churritz)";
540      #endregion
541      #region Algorithm Configuration
542      ga.Name = "Island Genetic Algorithm - TSP";
543      ga.Description = "An island genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
544      ga.Problem = tspProblem;
545      ConfigureIslandGeneticAlgorithmParameters<ProportionalSelector, OrderCrossover2, InversionManipulator,
546        UnidirectionalRingMigrator, BestSelector, WorstReplacer>(
547        ga, 100, 1, 1000, 0.05, 5, 50, 0.25);
548
549      ga.Analyzer.Operators.SetItemCheckedState(ga.Analyzer.Operators
550        .OfType<TSPAlleleFrequencyAnalyzer>()
551        .Single(), false);
552      ga.Analyzer.Operators.SetItemCheckedState(ga.Analyzer.Operators
553        .OfType<TSPPopulationDiversityAnalyzer>()
554        .Single(), false);
555      #endregion
556      return ga;
557    }
558    #endregion
559    #endregion
560
561    #region LS
562    #region Knapsack
563    [TestMethod]
564    public void CreateLocalSearchKnapsackSampleTest() {
565      var ls = CreateLocalSearchKnapsackSample();
566      XmlGenerator.Serialize(ls, "../../LS_Knapsack.hl");
567    }
568    [TestMethod]
569    public void RunLocalSearchKnapsackSampleTest() {
570      var ls = CreateLocalSearchKnapsackSample();
571      ls.SetSeedRandomly.Value = false;
572      RunAlgorithm(ls);
573      Assert.AreEqual(345, GetDoubleResult(ls, "BestQuality"));
574      Assert.AreEqual(340.70731707317071, GetDoubleResult(ls, "CurrentAverageQuality"));
575      Assert.AreEqual(337, GetDoubleResult(ls, "CurrentWorstQuality"));
576      Assert.AreEqual(82000, GetIntResult(ls, "EvaluatedMoves"));
577    }
578
579    private LocalSearch CreateLocalSearchKnapsackSample() {
580      LocalSearch ls = new LocalSearch();
581      #region Problem Configuration
582      KnapsackProblem problem = new KnapsackProblem();
583      problem.BestKnownQuality = new DoubleValue(362);
584      problem.BestKnownSolution = new HeuristicLab.Encodings.BinaryVectorEncoding.BinaryVector(new bool[] {
585       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});
586      problem.Evaluator = new KnapsackEvaluator();
587      problem.SolutionCreator = new RandomBinaryVectorCreator();
588      problem.KnapsackCapacity.Value = 297;
589      problem.Maximization.Value = true;
590      problem.Penalty.Value = 1;
591      problem.Values = new IntArray(new int[] {
592  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});
593      problem.Weights = new IntArray(new int[] {
594 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});
595      problem.Name = "Knapsack Problem";
596      problem.Description = "Represents a Knapsack problem.";
597      #endregion
598      #region Algorithm Configuration
599      ls.Name = "Local Search - Knapsack";
600      ls.Description = "A local search algorithm that solves a randomly generated Knapsack problem";
601      ls.Problem = problem;
602      ls.MaximumIterations.Value = 1000;
603      ls.MoveEvaluator = ls.MoveEvaluatorParameter.ValidValues
604        .OfType<KnapsackOneBitflipMoveEvaluator>()
605        .Single();
606      ls.MoveGenerator = ls.MoveGeneratorParameter.ValidValues
607        .OfType<ExhaustiveOneBitflipMoveGenerator>()
608        .Single();
609      ls.MoveMaker = ls.MoveMakerParameter.ValidValues
610        .OfType<OneBitflipMoveMaker>()
611        .Single();
612      ls.SampleSize.Value = 100;
613      ls.Seed.Value = 0;
614      ls.SetSeedRandomly.Value = true;
615      #endregion
616      ls.Engine = new ParallelEngine();
617      return ls;
618    }
619    #endregion
620    #endregion
621
622    #region PSO
623    #region Schwefel
624    [TestMethod]
625    public void CreatePsoSchwefelSampleTest() {
626      var pso = CreatePsoSchwefelSample();
627      XmlGenerator.Serialize(pso, "../../PSO_Schwefel.hl");
628    }
629    [TestMethod]
630    public void RunPsoSchwefelSampleTest() {
631      var pso = CreatePsoSchwefelSample();
632      pso.SetSeedRandomly.Value = false;
633      RunAlgorithm(pso);
634      if (!Environment.Is64BitProcess) {
635        Assert.AreEqual(119.30888659302838, GetDoubleResult(pso, "BestQuality"));
636        Assert.AreEqual(140.71570105946438, GetDoubleResult(pso, "CurrentAverageQuality"));
637        Assert.AreEqual(220.956806502853, GetDoubleResult(pso, "CurrentWorstQuality"));
638        Assert.AreEqual(1000, GetIntResult(pso, "Iterations"));
639      } else {
640        Assert.AreEqual(118.58396881972624, GetDoubleResult(pso, "BestQuality"));
641        Assert.AreEqual(139.43946864779372, GetDoubleResult(pso, "CurrentAverageQuality"));
642        Assert.AreEqual(217.14654589055152, GetDoubleResult(pso, "CurrentWorstQuality"));
643        Assert.AreEqual(1000, GetIntResult(pso, "Iterations"));
644      }
645    }
646    private ParticleSwarmOptimization CreatePsoSchwefelSample() {
647      ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
648      #region Problem Configuration
649      var problem = new SingleObjectiveTestFunctionProblem();
650      problem.BestKnownQuality.Value = 0.0;
651      problem.BestKnownSolutionParameter.Value = new RealVector(new double[] { 420.968746, 420.968746 });
652      problem.Bounds = new DoubleMatrix(new double[,] { { -500, 500 } });
653      problem.Evaluator = new SchwefelEvaluator();
654      problem.Maximization.Value = false;
655      problem.ProblemSize.Value = 2;
656      problem.SolutionCreator = new UniformRandomRealVectorCreator();
657      #endregion
658      #region Algorithm Configuration
659      pso.Name = "Particle Swarm Optimization - Schwefel";
660      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)";
661      pso.Problem = problem;
662      pso.Inertia.Value = 10;
663      pso.MaxIterations.Value = 1000;
664      pso.NeighborBestAttraction.Value = 0.5;
665      pso.PersonalBestAttraction.Value = -0.01;
666      pso.SwarmSize.Value = 50;
667
668      var inertiaUpdater = pso.InertiaUpdaterParameter.ValidValues
669        .OfType<ExponentialDiscreteDoubleValueModifier>()
670        .Single();
671      inertiaUpdater.StartValueParameter.Value = new DoubleValue(10);
672      inertiaUpdater.EndValueParameter.Value = new DoubleValue(1);
673      pso.InertiaUpdater = inertiaUpdater;
674
675      pso.ParticleCreator = pso.ParticleCreatorParameter.ValidValues
676        .OfType<RealVectorParticleCreator>()
677        .Single();
678      var swarmUpdater = pso.SwarmUpdaterParameter.ValidValues
679        .OfType<RealVectorSwarmUpdater>()
680        .Single();
681      swarmUpdater.VelocityBoundsIndexParameter.ActualName = "Iterations";
682      swarmUpdater.VelocityBoundsParameter.Value = new DoubleMatrix(new double[,] { { -10, 10 } });
683      swarmUpdater.VelocityBoundsStartValueParameter.Value = new DoubleValue(10.0);
684      swarmUpdater.VelocityBoundsEndValueParameter.Value = new DoubleValue(1.0);
685      swarmUpdater.VelocityBoundsScalingOperatorParameter.Value = swarmUpdater.VelocityBoundsScalingOperatorParameter.ValidValues
686        .OfType<ExponentialDiscreteDoubleValueModifier>()
687        .Single();
688
689      pso.TopologyInitializer = null;
690      pso.TopologyUpdater = null;
691      pso.SwarmUpdater = swarmUpdater;
692      pso.Seed.Value = 0;
693      pso.SetSeedRandomly.Value = true;
694      #endregion
695      pso.Engine = new ParallelEngine();
696      return pso;
697    }
698    #endregion
699    #endregion
700
701    #region SA
702    #region Rastrigin
703    [TestMethod]
704    public void CreateSimulatedAnnealingRastriginSampleTest() {
705      var sa = CreateSimulatedAnnealingRastriginSample();
706      XmlGenerator.Serialize(sa, "../../SA_Rastrigin.hl");
707    }
708    [TestMethod]
709    public void RunSimulatedAnnealingRastriginSampleTest() {
710      var sa = CreateSimulatedAnnealingRastriginSample();
711      sa.SetSeedRandomly.Value = false;
712      RunAlgorithm(sa);
713      Assert.AreEqual(0.00014039606034543795, GetDoubleResult(sa, "BestQuality"));
714      Assert.AreEqual(5000, GetIntResult(sa, "EvaluatedMoves"));
715    }
716    private SimulatedAnnealing CreateSimulatedAnnealingRastriginSample() {
717      SimulatedAnnealing sa = new SimulatedAnnealing();
718      #region Problem Configuration
719      var problem = new SingleObjectiveTestFunctionProblem();
720      problem.BestKnownQuality.Value = 0.0;
721      problem.BestKnownSolutionParameter.Value = new RealVector(new double[] { 0, 0 });
722      problem.Bounds = new DoubleMatrix(new double[,] { { -5.12, 5.12 } });
723      problem.Evaluator = new RastriginEvaluator();
724      problem.Maximization.Value = false;
725      problem.ProblemSize.Value = 2;
726      problem.SolutionCreator = new UniformRandomRealVectorCreator();
727      #endregion
728      #region Algorithm Configuration
729      sa.Name = "Simulated Annealing - Rastrigin";
730      sa.Description = "A simulated annealing algorithm that solves the 2-dimensional Rastrigin test function";
731      sa.Problem = problem;
732      var annealingOperator = sa.AnnealingOperatorParameter.ValidValues
733        .OfType<ExponentialDiscreteDoubleValueModifier>()
734        .Single();
735      annealingOperator.StartIndexParameter.Value = new IntValue(0);
736      sa.AnnealingOperator = annealingOperator;
737
738      sa.EndTemperature.Value = 1E-6;
739      sa.InnerIterations.Value = 50;
740      sa.MaximumIterations.Value = 100;
741      var moveEvaluator = sa.MoveEvaluatorParameter.ValidValues
742        .OfType<RastriginAdditiveMoveEvaluator>()
743        .Single();
744      moveEvaluator.A.Value = 10;
745      sa.MoveEvaluator = moveEvaluator;
746
747      var moveGenerator = sa.MoveGeneratorParameter.ValidValues
748        .OfType<StochasticNormalMultiMoveGenerator>()
749        .Single();
750      moveGenerator.SigmaParameter.Value = new DoubleValue(1);
751      sa.MoveGenerator = moveGenerator;
752
753      sa.MoveMaker = sa.MoveMakerParameter.ValidValues
754        .OfType<AdditiveMoveMaker>()
755        .Single();
756
757      sa.Seed.Value = 0;
758      sa.SetSeedRandomly.Value = true;
759      sa.StartTemperature.Value = 1;
760      #endregion
761      sa.Engine = new ParallelEngine();
762      return sa;
763    }
764    #endregion
765    #endregion
766
767    #region TS
768    #region TSP
769    [TestMethod]
770    public void CreateTabuSearchTspSampleTest() {
771      var ts = CreateTabuSearchTspSample();
772      XmlGenerator.Serialize(ts, "../../TS_TSP.hl");
773    }
774    [TestMethod]
775    public void RunTabuSearchTspSampleTest() {
776      var ts = CreateTabuSearchTspSample();
777      ts.SetSeedRandomly.Value = false;
778      RunAlgorithm(ts);
779      Assert.AreEqual(6441, GetDoubleResult(ts, "BestQuality"));
780      Assert.AreEqual(7401.666666666667, GetDoubleResult(ts, "CurrentAverageQuality"));
781      Assert.AreEqual(8418, GetDoubleResult(ts, "CurrentWorstQuality"));
782      Assert.AreEqual(750000, GetIntResult(ts, "EvaluatedMoves"));
783    }
784
785    private TabuSearch CreateTabuSearchTspSample() {
786      TabuSearch ts = new TabuSearch();
787      #region Problem Configuration
788      var tspProblem = new TravelingSalesmanProblem();
789      tspProblem.ImportFromTSPLIB("ch130.tsp", "ch130.opt.tour", 6110);
790      tspProblem.Evaluator = new TSPRoundedEuclideanPathEvaluator();
791      tspProblem.SolutionCreator = new RandomPermutationCreator();
792      tspProblem.UseDistanceMatrix.Value = true;
793      tspProblem.Name = "ch130 TSP (imported from TSPLIB)";
794      tspProblem.Description = "130 city problem (Churritz)";
795      #endregion
796      #region Algorithm Configuration
797      ts.Name = "Tabu Search - TSP";
798      ts.Description = "A tabu search algorithm that solves the \"ch130\" TSP (imported from TSPLIB)";
799      ts.Problem = tspProblem;
800
801      ts.MaximumIterations.Value = 1000;
802      // move generator has to be set first
803      var moveGenerator = ts.MoveGeneratorParameter.ValidValues
804        .OfType<StochasticInversionMultiMoveGenerator>()
805        .Single();
806      ts.MoveGenerator = moveGenerator;
807      var moveEvaluator = ts.MoveEvaluatorParameter.ValidValues
808        .OfType<TSPInversionMoveRoundedEuclideanPathEvaluator>()
809        .Single();
810      ts.MoveEvaluator = moveEvaluator;
811      var moveMaker = ts.MoveMakerParameter.ValidValues
812        .OfType<InversionMoveMaker>()
813        .Single();
814      ts.MoveMaker = moveMaker;
815      ts.SampleSize.Value = 750;
816      ts.Seed.Value = 0;
817      ts.SetSeedRandomly.Value = true;
818
819      var tabuChecker = ts.TabuCheckerParameter.ValidValues
820        .OfType<InversionMoveSoftTabuCriterion>()
821        .Single();
822      tabuChecker.UseAspirationCriterion.Value = true;
823      ts.TabuChecker = tabuChecker;
824
825      var tabuMaker = ts.TabuMakerParameter.ValidValues
826        .OfType<InversionMoveTabuMaker>()
827        .Single();
828      ts.TabuMaker = tabuMaker;
829      ts.TabuTenure.Value = 60;
830
831      ts.Analyzer.Operators.SetItemCheckedState(ts.Analyzer.Operators
832        .OfType<TSPAlleleFrequencyAnalyzer>()
833        .Single(), false);
834      ts.Analyzer.Operators.SetItemCheckedState(ts.Analyzer.Operators
835        .OfType<TSPPopulationDiversityAnalyzer>()
836        .Single(), false);
837      #endregion
838      ts.Engine = new ParallelEngine();
839      return ts;
840    }
841    #endregion
842    #endregion
843
844    #region VNS
845    #region TSP
846    [TestMethod]
847    public void CreateVnsTspSampleTest() {
848      var vns = CreateVnsTspSample();
849      XmlGenerator.Serialize(vns, "../../VNS_TSP.hl");
850    }
851    [TestMethod]
852    public void RunVnsTspSampleTest() {
853      var vns = CreateVnsTspSample();
854      vns.SetSeedRandomly = false;
855      RunAlgorithm(vns);
856      Assert.AreEqual(867, GetDoubleResult(vns, "BestQuality"));
857      Assert.AreEqual(867, GetDoubleResult(vns, "CurrentAverageQuality"));
858      Assert.AreEqual(867, GetDoubleResult(vns, "CurrentWorstQuality"));
859      Assert.AreEqual(12975173, GetIntResult(vns, "EvaluatedSolutions"));
860    }
861
862    private VariableNeighborhoodSearch CreateVnsTspSample() {
863      VariableNeighborhoodSearch vns = new VariableNeighborhoodSearch();
864      #region Problem Configuration
865      TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
866      tspProblem.BestKnownSolution = new Permutation(PermutationTypes.Absolute, new int[] {
867117, 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,
868      });
869      tspProblem.Coordinates = new DoubleMatrix(new double[,] {
870{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}
871      });
872      tspProblem.BestKnownQuality = new DoubleValue(867);
873
874      tspProblem.Evaluator = new TSPRoundedEuclideanPathEvaluator();
875      tspProblem.SolutionCreator = new RandomPermutationCreator();
876      tspProblem.UseDistanceMatrix.Value = true;
877      tspProblem.Name = "Funny TSP";
878      tspProblem.Description = "Represents a symmetric Traveling Salesman Problem.";
879      #endregion
880      #region Algorithm Configuration
881      vns.Name = "Variable Neighborhood Search - TSP";
882      vns.Description = "A variable neighborhood search algorithm which solves a funny TSP instance";
883      vns.Problem = tspProblem;
884
885      var localImprovement = vns.LocalImprovementParameter.ValidValues
886        .OfType<LocalSearchImprovementOperator>()
887        .Single();
888      // move generator has to be set first
889      localImprovement.MoveGenerator = localImprovement.MoveGeneratorParameter.ValidValues
890        .OfType<StochasticInversionMultiMoveGenerator>()
891        .Single();
892      localImprovement.MoveEvaluator = localImprovement.MoveEvaluatorParameter.ValidValues
893        .OfType<TSPInversionMoveRoundedEuclideanPathEvaluator>()
894        .Single();
895      localImprovement.MoveMaker = localImprovement.MoveMakerParameter.ValidValues
896        .OfType<InversionMoveMaker>()
897        .Single();
898      localImprovement.SampleSizeParameter.Value = new IntValue(500);
899      vns.LocalImprovement = localImprovement;
900
901      vns.LocalImprovementMaximumIterations = 150;
902      vns.MaximumIterations = 25;
903      vns.Seed = 0;
904      vns.SetSeedRandomly = true;
905      var shakingOperator = vns.ShakingOperatorParameter.ValidValues
906        .OfType<PermutationShakingOperator>()
907        .Single();
908      shakingOperator.Operators.SetItemCheckedState(shakingOperator.Operators
909        .OfType<Swap2Manipulator>()
910        .Single(), false);
911      shakingOperator.Operators.SetItemCheckedState(shakingOperator.Operators
912        .OfType<Swap3Manipulator>()
913        .Single(), false);
914      vns.ShakingOperator = shakingOperator;
915      vns.Analyzer.Operators.SetItemCheckedState(vns.Analyzer.Operators
916        .OfType<TSPAlleleFrequencyAnalyzer>()
917        .Single(), false);
918      vns.Analyzer.Operators.SetItemCheckedState(vns.Analyzer.Operators
919        .OfType<TSPPopulationDiversityAnalyzer>()
920        .Single(), false);
921      #endregion
922      vns.Engine = new ParallelEngine();
923      return vns;
924    }
925    #endregion
926    #endregion
927
928    #region Helpers
929    private void ConfigureEvolutionStrategyParameters<R, M, SC, SR, SM>(EvolutionStrategy es, int popSize, int children, int parentsPerChild, int maxGens, bool plusSelection)
930      where R : ICrossover
931      where M : IManipulator
932      where SC : IStrategyParameterCreator
933      where SR : IStrategyParameterCrossover
934      where SM : IStrategyParameterManipulator {
935      es.PopulationSize.Value = popSize;
936      es.Children.Value = children;
937      es.ParentsPerChild.Value = parentsPerChild;
938      es.MaximumGenerations.Value = maxGens;
939      es.PlusSelection.Value = false;
940
941      es.Seed.Value = 0;
942      es.SetSeedRandomly.Value = true;
943
944      es.Recombinator = es.RecombinatorParameter.ValidValues
945        .OfType<R>()
946        .Single();
947
948      es.Mutator = es.MutatorParameter.ValidValues
949        .OfType<M>()
950        .Single();
951
952      es.StrategyParameterCreator = es.StrategyParameterCreatorParameter.ValidValues
953        .OfType<SC>()
954        .Single();
955      es.StrategyParameterCrossover = es.StrategyParameterCrossoverParameter.ValidValues
956        .OfType<SR>()
957        .Single();
958      es.StrategyParameterManipulator = es.StrategyParameterManipulatorParameter.ValidValues
959        .OfType<SM>()
960        .Single();
961      es.Engine = new ParallelEngine();
962    }
963
964    private void ConfigureGeneticAlgorithmParameters<S, C, M>(GeneticAlgorithm ga, int popSize, int elites, int maxGens, double mutationRate, int tournGroupSize = 0)
965      where S : ISelector
966      where C : ICrossover
967      where M : IManipulator {
968      ga.Elites.Value = elites;
969      ga.MaximumGenerations.Value = maxGens;
970      ga.MutationProbability.Value = mutationRate;
971      ga.PopulationSize.Value = popSize;
972      ga.Seed.Value = 0;
973      ga.SetSeedRandomly.Value = true;
974      ga.Selector = ga.SelectorParameter.ValidValues
975        .OfType<S>()
976        .Single();
977
978      ga.Crossover = ga.CrossoverParameter.ValidValues
979        .OfType<C>()
980        .Single();
981
982      ga.Mutator = ga.MutatorParameter.ValidValues
983        .OfType<M>()
984        .Single();
985
986      var tSelector = ga.Selector as TournamentSelector;
987      if (tSelector != null) {
988        tSelector.GroupSizeParameter.Value.Value = tournGroupSize;
989      }
990      ga.Engine = new ParallelEngine();
991    }
992
993    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)
994      where S : ISelector
995      where C : ICrossover
996      where M : IManipulator
997      where Mi : IMigrator
998      where MiS : ISelector
999      where MiR : IReplacer {
1000      ga.Elites.Value = elites;
1001      ga.MaximumGenerations.Value = maxGens;
1002      ga.MutationProbability.Value = mutationRate;
1003      ga.PopulationSize.Value = popSize;
1004      ga.NumberOfIslands.Value = numberOfIslands;
1005      ga.MigrationInterval.Value = migrationInterval;
1006      ga.MigrationRate.Value = migrationRate;
1007      ga.Seed.Value = 0;
1008      ga.SetSeedRandomly.Value = true;
1009      ga.Selector = ga.SelectorParameter.ValidValues
1010        .OfType<S>()
1011        .Single();
1012
1013      ga.Crossover = ga.CrossoverParameter.ValidValues
1014        .OfType<C>()
1015        .Single();
1016
1017      ga.Mutator = ga.MutatorParameter.ValidValues
1018        .OfType<M>()
1019        .Single();
1020      ga.Migrator = ga.MigratorParameter.ValidValues
1021        .OfType<Mi>()
1022        .Single();
1023      ga.EmigrantsSelector = ga.EmigrantsSelectorParameter.ValidValues
1024        .OfType<MiS>()
1025        .Single();
1026      ga.ImmigrationReplacer = ga.ImmigrationReplacerParameter.ValidValues
1027        .OfType<MiR>()
1028        .Single();
1029      ga.Engine = new ParallelEngine();
1030    }
1031
1032
1033    private void RunAlgorithm(IAlgorithm a) {
1034      var trigger = new EventWaitHandle(false, EventResetMode.ManualReset);
1035      Exception ex = null;
1036      a.Stopped += (src, e) => { trigger.Set(); };
1037      a.ExceptionOccurred += (src, e) => { ex = e.Value; trigger.Set(); };
1038      a.Prepare();
1039      a.Start();
1040      trigger.WaitOne();
1041
1042      Assert.AreEqual(ex, null);
1043    }
1044
1045    private double GetDoubleResult(IAlgorithm a, string resultName) {
1046      return ((DoubleValue)a.Results[resultName].Value).Value;
1047    }
1048    private int GetIntResult(IAlgorithm a, string resultName) {
1049      return ((IntValue)a.Results[resultName].Value).Value;
1050    }
1051    #endregion
1052  }
1053}
Note: See TracBrowser for help on using the repository browser.