[6441] | 1 | using System;
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| 2 | using System.Text;
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| 3 | using System.Collections.Generic;
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| 4 | using System.Linq;
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| 5 | using Microsoft.VisualStudio.TestTools.UnitTesting;
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| 6 | using HeuristicLab.Algorithms.GeneticAlgorithm;
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| 7 | using HeuristicLab.Problems.ArtificialAnt;
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| 8 | using HeuristicLab.Selection;
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| 9 | using HeuristicLab.Data;
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| 10 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 11 | using HeuristicLab.Persistence.Default.Xml;
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| 12 | using HeuristicLab.Optimization;
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| 13 | using System.Threading;
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| 14 | using HeuristicLab.ParallelEngine;
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| 15 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
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| 16 | using HeuristicLab.Problems.DataAnalysis;
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| 17 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 18 | using System.IO;
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| 19 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Classification;
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| 20 | using HeuristicLab.Problems.TravelingSalesman;
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| 21 | using HeuristicLab.Encodings.PermutationEncoding;
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| 22 | using HeuristicLab.Problems.VehicleRouting;
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| 23 | using HeuristicLab.Problems.VehicleRouting.Encodings.Potvin;
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| 24 | using HeuristicLab.Problems.VehicleRouting.Encodings;
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| 25 | using HeuristicLab.Problems.VehicleRouting.Encodings.General;
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| 26 |
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| 27 | namespace HeuristicLab_33.Tests {
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| 28 | [TestClass]
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| 29 | public class GeneticAlgorithmSamplesTest {
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| 30 | [TestMethod]
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| 31 | public void CreateTSPSample() {
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| 32 | GeneticAlgorithm ga = new GeneticAlgorithm();
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| 33 | #region problem configuration
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| 34 | TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
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| 35 | // import and configure TSP data
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| 36 | string ch130FileName = Path.GetTempFileName() + ".tsp";// for silly parser constraints
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| 37 | using (var writer = File.CreateText(ch130FileName)) {
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| 38 | writer.Write(HeuristicLab_33.Tests.Properties.Resources.ch130);
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| 39 | }
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| 40 | string ch130OptTourFileName = Path.GetTempFileName() + ".opt.tour"; // for silly parser constraints
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| 41 | using (var writer = File.CreateText(ch130OptTourFileName)) {
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| 42 | writer.Write(HeuristicLab_33.Tests.Properties.Resources.ch130_opt);
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| 43 | }
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| 44 |
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| 45 | tspProblem.ImportFromTSPLIB(ch130FileName, ch130OptTourFileName, 6110);
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| 46 | tspProblem.Evaluator = new TSPRoundedEuclideanPathEvaluator();
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| 47 | tspProblem.SolutionCreator = new RandomPermutationCreator();
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| 48 | tspProblem.UseDistanceMatrix.Value = true;
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| 49 | tspProblem.Name = "ch130 TSP (imported from TSPLIB)";
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| 50 | tspProblem.Description = "130 city problem (Churritz)";
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| 51 | #endregion
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| 52 | #region algorithm configuration
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| 53 | ga.Name = "Genetic Algorithm - TSP";
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| 54 | ga.Description = "A genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
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| 55 | ga.Problem = tspProblem;
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| 56 | ConfigureGeneticAlgorithmParameters<ProportionalSelector, OrderCrossover2, InversionManipulator>(
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| 57 | ga, 100, 1, 1000, 0.05);
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| 58 |
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| 59 | ga.Analyzer.Operators.SetItemCheckedState(ga.Analyzer.Operators
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| 60 | .OfType<TSPAlleleFrequencyAnalyzer>()
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| 61 | .Single(), false);
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| 62 | ga.Analyzer.Operators.SetItemCheckedState(ga.Analyzer.Operators
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| 63 | .OfType<TSPPopulationDiversityAnalyzer>()
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| 64 | .Single(), false);
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| 65 | #endregion
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| 66 |
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| 67 | XmlGenerator.Serialize(ga, "../../GA_TSP.hl");
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| 68 |
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| 69 | RunAlgorithm(ga);
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| 70 | }
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| 71 | [TestMethod]
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| 72 | public void CreateVRPSample() {
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| 73 | GeneticAlgorithm ga = new GeneticAlgorithm();
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| 74 | #region problem configuration
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| 75 | VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
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| 76 | // import and configure VRP data
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| 77 | string c101FileName = Path.GetTempFileName();
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| 78 | using (var writer = File.CreateText(c101FileName)) {
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| 79 | writer.Write(HeuristicLab_33.Tests.Properties.Resources.C101);
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| 80 | }
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| 81 | // import and configure VRP data
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| 82 | string c101BestSolutionFileName = Path.GetTempFileName();
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| 83 | using (var writer = File.CreateText(c101BestSolutionFileName)) {
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| 84 | writer.Write(HeuristicLab_33.Tests.Properties.Resources.C101_opt);
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| 85 | }
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| 86 |
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| 87 | vrpProblem.ImportFromSolomon(c101FileName);
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| 88 | vrpProblem.ImportSolution(c101BestSolutionFileName);
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| 89 | vrpProblem.Name = "C101 VRP (imported from Solomon)";
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| 90 | vrpProblem.Description = "Represents a Vehicle Routing Problem.";
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| 91 | vrpProblem.DistanceFactorParameter.Value.Value = 1;
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| 92 | vrpProblem.FleetUsageFactorParameter.Value.Value = 100;
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| 93 | vrpProblem.OverloadPenaltyParameter.Value.Value = 100;
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| 94 | vrpProblem.TardinessPenaltyParameter.Value.Value = 100;
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| 95 | vrpProblem.TimeFactorParameter.Value.Value = 0;
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| 96 | vrpProblem.Evaluator = new VRPEvaluator();
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| 97 | vrpProblem.MaximizationParameter.Value.Value = false;
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| 98 | vrpProblem.SolutionCreator = new RandomCreator();
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| 99 | vrpProblem.UseDistanceMatrix.Value = true;
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| 100 | vrpProblem.Vehicles.Value = 25;
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| 101 | #endregion
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| 102 | #region algorithm configuration
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| 103 | ga.Name = "Genetic Algorithm - VRP";
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| 104 | ga.Description = "A genetic algorithm which solves the \"C101\" vehicle routing problem (imported from Solomon)";
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| 105 | ga.Problem = vrpProblem;
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| 106 | ConfigureGeneticAlgorithmParameters<TournamentSelector, MultiVRPSolutionCrossover, MultiVRPSolutionManipulator>(
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| 107 | ga, 100, 1, 1000, 0.05, 3);
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| 108 |
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| 109 | var xOver = (MultiVRPSolutionCrossover)ga.Crossover;
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| 110 | foreach (var op in xOver.Operators) {
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| 111 | xOver.Operators.SetItemCheckedState(op, false);
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| 112 | }
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| 113 | xOver.Operators.SetItemCheckedState(xOver.Operators
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| 114 | .OfType<PotvinRouteBasedCrossover>()
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| 115 | .Single(), true);
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| 116 | xOver.Operators.SetItemCheckedState(xOver.Operators
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| 117 | .OfType<PotvinSequenceBasedCrossover>()
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| 118 | .Single(), true);
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| 119 |
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| 120 | var manipulator = (MultiVRPSolutionManipulator)ga.Mutator;
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| 121 | foreach (var op in manipulator.Operators) {
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| 122 | manipulator.Operators.SetItemCheckedState(op, false);
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| 123 | }
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| 124 | manipulator.Operators.SetItemCheckedState(manipulator.Operators
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| 125 | .OfType<PotvinOneLevelExchangeMainpulator>()
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| 126 | .Single(), true);
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| 127 | manipulator.Operators.SetItemCheckedState(manipulator.Operators
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| 128 | .OfType<PotvinTwoLevelExchangeManipulator>()
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| 129 | .Single(), true);
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| 130 | #endregion
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| 131 |
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| 132 | XmlGenerator.Serialize(ga, "../../GA_VRP.hl");
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| 133 |
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| 134 | RunAlgorithm(ga);
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| 135 | }
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| 136 |
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| 137 | [TestMethod]
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| 138 | public void CreateArtificialAntSample() {
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| 139 | GeneticAlgorithm ga = new GeneticAlgorithm();
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| 140 | #region problem configuration
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| 141 | ArtificialAntProblem antProblem = new ArtificialAntProblem();
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| 142 | antProblem.BestKnownQuality.Value = 89;
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| 143 | antProblem.MaxExpressionDepth.Value = 10;
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| 144 | antProblem.MaxExpressionLength.Value = 100;
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| 145 | antProblem.MaxFunctionArguments.Value = 3;
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| 146 | antProblem.MaxFunctionDefinitions.Value = 3;
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| 147 | antProblem.MaxTimeSteps.Value = 600;
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| 148 | #endregion
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| 149 | #region algorithm configuration
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| 150 | ga.Name = "Genetic Programming - Artificial Ant";
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| 151 | ga.Description = "A standard genetic programming algorithm to solve the artificial ant problem (Santa-Fe trail)";
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| 152 | ga.Problem = antProblem;
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| 153 | ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeArchitectureManipulator>(
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| 154 | ga, 1000, 1, 100, 0.15, 5);
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| 155 | var mutator = (MultiSymbolicExpressionTreeArchitectureManipulator)ga.Mutator;
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| 156 | mutator.Operators.SetItemCheckedState(mutator.Operators
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| 157 | .OfType<FullTreeShaker>()
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| 158 | .Single(), false);
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| 159 | mutator.Operators.SetItemCheckedState(mutator.Operators
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| 160 | .OfType<OnePointShaker>()
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| 161 | .Single(), false);
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[6442] | 162 | mutator.Operators.SetItemCheckedState(mutator.Operators
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| 163 | .OfType<ArgumentDeleter>()
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| 164 | .Single(), false);
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| 165 | mutator.Operators.SetItemCheckedState(mutator.Operators
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| 166 | .OfType<SubroutineDeleter>()
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| 167 | .Single(), false);
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[6441] | 168 | #endregion
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| 169 |
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| 170 | XmlGenerator.Serialize(ga, "../../SGP_SantaFe.hl");
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| 171 |
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| 172 | RunAlgorithm(ga);
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| 173 | }
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| 174 |
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| 175 | [TestMethod]
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| 176 | public void CreateSymbolicRegressionSample() {
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| 177 | GeneticAlgorithm ga = new GeneticAlgorithm();
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| 178 | #region problem configuration
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| 179 | SymbolicRegressionSingleObjectiveProblem symbRegProblem = new SymbolicRegressionSingleObjectiveProblem();
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| 180 | symbRegProblem.Name = "Tower Symbolic Regression Problem";
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| 181 | symbRegProblem.Description = "Tower Dataset (downloaded from: http://vanillamodeling.com/realproblems.html)";
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| 182 | // import and configure problem data
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| 183 | string filename = Path.GetTempFileName();
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| 184 | using (var writer = File.CreateText(filename)) {
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| 185 | writer.Write(HeuristicLab_33.Tests.Properties.Resources.TowerData);
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| 186 | }
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| 187 | var towerProblemData = RegressionProblemData.ImportFromFile(filename);
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| 188 | towerProblemData.TargetVariableParameter.Value = towerProblemData.TargetVariableParameter.ValidValues
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| 189 | .First(v => v.Value == "towerResponse");
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| 190 | towerProblemData.InputVariables.SetItemCheckedState(
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| 191 | towerProblemData.InputVariables.Single(x => x.Value == "x1"), true);
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| 192 | towerProblemData.InputVariables.SetItemCheckedState(
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| 193 | towerProblemData.InputVariables.Single(x => x.Value == "x7"), false);
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| 194 | towerProblemData.InputVariables.SetItemCheckedState(
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| 195 | towerProblemData.InputVariables.Single(x => x.Value == "x11"), false);
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| 196 | towerProblemData.InputVariables.SetItemCheckedState(
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| 197 | towerProblemData.InputVariables.Single(x => x.Value == "x16"), false);
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| 198 | towerProblemData.InputVariables.SetItemCheckedState(
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| 199 | towerProblemData.InputVariables.Single(x => x.Value == "x21"), false);
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| 200 | towerProblemData.InputVariables.SetItemCheckedState(
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| 201 | towerProblemData.InputVariables.Single(x => x.Value == "x25"), false);
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| 202 | towerProblemData.InputVariables.SetItemCheckedState(
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| 203 | towerProblemData.InputVariables.Single(x => x.Value == "towerResponse"), false);
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| 204 | towerProblemData.TrainingPartition.Start = 0;
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| 205 | towerProblemData.TrainingPartition.End = 4000;
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| 206 | towerProblemData.TestPartition.Start = 4000;
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| 207 | towerProblemData.TestPartition.End = 4999;
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| 208 | towerProblemData.Name = "Data imported from towerData.txt";
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| 209 | 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";
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| 210 | symbRegProblem.ProblemData = towerProblemData;
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| 211 |
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| 212 | // configure grammar
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| 213 | var grammar = new TypeCoherentExpressionGrammar();
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| 214 | grammar.Symbols.OfType<Sine>().Single().InitialFrequency = 0.0;
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| 215 | grammar.Symbols.OfType<Cosine>().Single().InitialFrequency = 0.0;
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| 216 | grammar.Symbols.OfType<Tangent>().Single().InitialFrequency = 0.0;
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| 217 | grammar.Symbols.OfType<IfThenElse>().Single().InitialFrequency = 0.0;
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| 218 | grammar.Symbols.OfType<GreaterThan>().Single().InitialFrequency = 0.0;
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| 219 | grammar.Symbols.OfType<LessThan>().Single().InitialFrequency = 0.0;
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| 220 | grammar.Symbols.OfType<And>().Single().InitialFrequency = 0.0;
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| 221 | grammar.Symbols.OfType<Or>().Single().InitialFrequency = 0.0;
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| 222 | grammar.Symbols.OfType<Not>().Single().InitialFrequency = 0.0;
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| 223 | grammar.Symbols.OfType<TimeLag>().Single().InitialFrequency = 0.0;
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| 224 | grammar.Symbols.OfType<Integral>().Single().InitialFrequency = 0.0;
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| 225 | grammar.Symbols.OfType<Derivative>().Single().InitialFrequency = 0.0;
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| 226 | grammar.Symbols.OfType<LaggedVariable>().Single().InitialFrequency = 0.0;
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| 227 | grammar.Symbols.OfType<VariableCondition>().Single().InitialFrequency = 0.0;
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| 228 | var varSymbol = grammar.Symbols.OfType<Variable>().Where(x => !(x is LaggedVariable)).Single();
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| 229 | varSymbol.WeightMu = 1.0;
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| 230 | varSymbol.WeightSigma = 1.0;
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| 231 | varSymbol.WeightManipulatorMu = 0.0;
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| 232 | varSymbol.WeightManipulatorSigma = 0.05;
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| 233 | varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
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| 234 | var constSymbol = grammar.Symbols.OfType<Constant>().Single();
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| 235 | constSymbol.MaxValue = 20;
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| 236 | constSymbol.MinValue = -20;
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| 237 | constSymbol.ManipulatorMu = 0.0;
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| 238 | constSymbol.ManipulatorSigma = 1;
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| 239 | constSymbol.MultiplicativeManipulatorSigma = 0.03;
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| 240 | symbRegProblem.SymbolicExpressionTreeGrammar = grammar;
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| 241 |
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| 242 | // configure remaining problem parameters
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| 243 | symbRegProblem.BestKnownQuality.Value = 0.97;
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| 244 | symbRegProblem.FitnessCalculationPartition.Start = 0;
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| 245 | symbRegProblem.FitnessCalculationPartition.End = 2800;
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| 246 | symbRegProblem.ValidationPartition.Start = 2800;
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| 247 | symbRegProblem.ValidationPartition.End = 4000;
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| 248 | symbRegProblem.RelativeNumberOfEvaluatedSamples.Value = 0.3;
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| 249 | symbRegProblem.MaximumSymbolicExpressionTreeLength.Value = 150;
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| 250 | symbRegProblem.MaximumSymbolicExpressionTreeDepth.Value = 12;
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| 251 | symbRegProblem.MaximumFunctionDefinitions.Value = 0;
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| 252 | symbRegProblem.MaximumFunctionArguments.Value = 0;
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| 253 |
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| 254 | symbRegProblem.EvaluatorParameter.Value = new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator();
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| 255 | #endregion
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| 256 | #region algorithm configuration
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| 257 | ga.Problem = symbRegProblem;
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| 258 | ga.Name = "Genetic Programming - Symbolic Regression";
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| 259 | ga.Description = "A standard genetic programming algorithm to solve a symbolic regression problem (tower dataset)";
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| 260 | ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
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| 261 | ga, 1000, 1, 100, 0.15, 5);
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| 262 | var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
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| 263 | mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
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| 264 | mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
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| 265 |
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| 266 | ga.Analyzer.Operators.SetItemCheckedState(
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| 267 | ga.Analyzer.Operators
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| 268 | .OfType<SymbolicRegressionSingleObjectiveOverfittingAnalyzer>()
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| 269 | .Single(), false);
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| 270 | #endregion
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| 271 |
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| 272 | XmlGenerator.Serialize(ga, "../../SGP_SymbReg.hl");
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| 273 |
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| 274 | RunAlgorithm(ga);
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| 275 | }
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| 276 |
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| 277 | [TestMethod]
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| 278 | public void CreateSymbolicClassificationSample() {
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| 279 | GeneticAlgorithm ga = new GeneticAlgorithm();
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| 280 | #region problem configuration
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| 281 | SymbolicClassificationSingleObjectiveProblem symbClassProblem = new SymbolicClassificationSingleObjectiveProblem();
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| 282 | symbClassProblem.Name = "Mammography Classification Problem";
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| 283 | symbClassProblem.Description = "Mammography dataset imported from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass)";
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| 284 | // import and configure problem data
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| 285 | string filename = Path.GetTempFileName();
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| 286 | using (var writer = File.CreateText(filename)) {
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| 287 | writer.Write(HeuristicLab_33.Tests.Properties.Resources.MammographicMasses);
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| 288 | }
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| 289 | var mammoData = ClassificationProblemData.ImportFromFile(filename);
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| 290 | mammoData.TargetVariableParameter.Value = mammoData.TargetVariableParameter.ValidValues
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| 291 | .First(v => v.Value == "Severity");
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| 292 | mammoData.InputVariables.SetItemCheckedState(
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| 293 | mammoData.InputVariables.Single(x => x.Value == "BI-RADS"), false);
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| 294 | mammoData.InputVariables.SetItemCheckedState(
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| 295 | mammoData.InputVariables.Single(x => x.Value == "Age"), true);
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| 296 | mammoData.InputVariables.SetItemCheckedState(
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| 297 | mammoData.InputVariables.Single(x => x.Value == "Shape"), true);
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| 298 | mammoData.InputVariables.SetItemCheckedState(
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| 299 | mammoData.InputVariables.Single(x => x.Value == "Margin"), true);
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| 300 | mammoData.InputVariables.SetItemCheckedState(
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| 301 | mammoData.InputVariables.Single(x => x.Value == "Density"), true);
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| 302 | mammoData.InputVariables.SetItemCheckedState(
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| 303 | mammoData.InputVariables.Single(x => x.Value == "Severity"), false);
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| 304 | mammoData.TrainingPartition.Start = 0;
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| 305 | mammoData.TrainingPartition.End = 800;
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| 306 | mammoData.TestPartition.Start = 800;
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| 307 | mammoData.TestPartition.End = 961;
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| 308 | mammoData.Name = "Data imported from mammographic_masses.csv";
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| 309 | mammoData.Description = "Original dataset: http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass, missing values have been replaced with median values.";
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| 310 | symbClassProblem.ProblemData = mammoData;
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| 311 |
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| 312 | // configure grammar
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| 313 | var grammar = new TypeCoherentExpressionGrammar();
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| 314 | grammar.Symbols.OfType<Sine>().Single().InitialFrequency = 0.0;
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| 315 | grammar.Symbols.OfType<Cosine>().Single().InitialFrequency = 0.0;
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| 316 | grammar.Symbols.OfType<Tangent>().Single().InitialFrequency = 0.0;
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| 317 | grammar.Symbols.OfType<Power>().Single().InitialFrequency = 0.0;
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| 318 | grammar.Symbols.OfType<Root>().Single().InitialFrequency = 0.0;
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| 319 | grammar.Symbols.OfType<TimeLag>().Single().InitialFrequency = 0.0;
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| 320 | grammar.Symbols.OfType<Integral>().Single().InitialFrequency = 0.0;
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| 321 | grammar.Symbols.OfType<Derivative>().Single().InitialFrequency = 0.0;
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| 322 | grammar.Symbols.OfType<LaggedVariable>().Single().InitialFrequency = 0.0;
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| 323 | grammar.Symbols.OfType<VariableCondition>().Single().InitialFrequency = 0.0;
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| 324 | var varSymbol = grammar.Symbols.OfType<Variable>().Where(x => !(x is LaggedVariable)).Single();
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| 325 | varSymbol.WeightMu = 1.0;
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| 326 | varSymbol.WeightSigma = 1.0;
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| 327 | varSymbol.WeightManipulatorMu = 0.0;
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| 328 | varSymbol.WeightManipulatorSigma = 0.05;
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| 329 | varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
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| 330 | var constSymbol = grammar.Symbols.OfType<Constant>().Single();
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| 331 | constSymbol.MaxValue = 20;
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| 332 | constSymbol.MinValue = -20;
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| 333 | constSymbol.ManipulatorMu = 0.0;
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| 334 | constSymbol.ManipulatorSigma = 1;
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| 335 | constSymbol.MultiplicativeManipulatorSigma = 0.03;
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| 336 | symbClassProblem.SymbolicExpressionTreeGrammar = grammar;
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| 337 |
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| 338 | // configure remaining problem parameters
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| 339 | symbClassProblem.BestKnownQuality.Value = 0.0;
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| 340 | symbClassProblem.FitnessCalculationPartition.Start = 0;
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| 341 | symbClassProblem.FitnessCalculationPartition.End = 400;
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| 342 | symbClassProblem.ValidationPartition.Start = 400;
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| 343 | symbClassProblem.ValidationPartition.End = 800;
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| 344 | symbClassProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
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| 345 | symbClassProblem.MaximumSymbolicExpressionTreeLength.Value = 100;
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| 346 | symbClassProblem.MaximumSymbolicExpressionTreeDepth.Value = 10;
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| 347 | symbClassProblem.MaximumFunctionDefinitions.Value = 0;
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| 348 | symbClassProblem.MaximumFunctionArguments.Value = 0;
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| 349 | symbClassProblem.EvaluatorParameter.Value = new SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator();
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| 350 | #endregion
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| 351 | #region algorithm configuration
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| 352 | ga.Problem = symbClassProblem;
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| 353 | ga.Name = "Genetic Programming - Symbolic Classification";
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| 354 | ga.Description = "A standard genetic programming algorithm to solve a classification problem (Mammographic+Mass dataset)";
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| 355 | ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
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| 356 | ga, 1000, 1, 100, 0.15, 5
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| 357 | );
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| 358 |
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| 359 | var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
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| 360 | mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
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| 361 | mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
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| 362 |
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| 363 | ga.Analyzer.Operators.SetItemCheckedState(
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| 364 | ga.Analyzer.Operators
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| 365 | .OfType<SymbolicClassificationSingleObjectiveOverfittingAnalyzer>()
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| 366 | .Single(), false);
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| 367 | #endregion
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| 368 |
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| 369 | XmlGenerator.Serialize(ga, "../../SGP_SymbClass.hl");
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| 370 |
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| 371 | RunAlgorithm(ga);
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| 372 | }
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| 373 |
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| 374 | private void ConfigureGeneticAlgorithmParameters<S, C, M>(GeneticAlgorithm ga, int popSize, int elites, int maxGens, double mutationRate, int tournGroupSize = 0)
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| 375 | where S : ISelector
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| 376 | where C : ICrossover
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| 377 | where M : IManipulator {
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| 378 | ga.Elites.Value = elites;
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| 379 | ga.MaximumGenerations.Value = maxGens;
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| 380 | ga.MutationProbability.Value = mutationRate;
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| 381 | ga.PopulationSize.Value = popSize;
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| 382 | ga.Seed.Value = 0;
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| 383 | ga.SetSeedRandomly.Value = true;
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| 384 | ga.Selector = ga.SelectorParameter.ValidValues
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| 385 | .OfType<S>()
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| 386 | .Single();
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| 387 |
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| 388 | ga.Crossover = ga.CrossoverParameter.ValidValues
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| 389 | .OfType<C>()
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| 390 | .Single();
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| 391 |
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| 392 | ga.Mutator = ga.MutatorParameter.ValidValues
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| 393 | .OfType<M>()
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| 394 | .Single();
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| 395 |
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| 396 | var tSelector = ga.Selector as TournamentSelector;
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| 397 | if (tSelector != null) {
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| 398 | tSelector.GroupSizeParameter.Value.Value = 5;
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| 399 | }
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| 400 | ga.Engine = new ParallelEngine();
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| 401 | }
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| 402 |
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| 403 |
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| 404 | private void RunAlgorithm(IAlgorithm a) {
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| 405 | var trigger = new EventWaitHandle(false, EventResetMode.ManualReset);
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| 406 | Exception ex = null;
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| 407 | a.Stopped += (src, e) => { trigger.Set(); };
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| 408 | a.ExceptionOccurred += (src, e) => { ex = e.Value; };
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| 409 | a.Prepare();
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| 410 | a.Start();
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| 411 | trigger.WaitOne();
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| 412 |
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| 413 | Assert.AreEqual(ex, null);
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| 414 | }
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| 415 | }
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| 416 | }
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