1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Linq;
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24 | using HeuristicLab.Analysis;
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25 | using HeuristicLab.Common;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Data;
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28 | using HeuristicLab.Encodings.ConditionActionEncoding;
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29 | using HeuristicLab.Operators;
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30 | using HeuristicLab.Optimization;
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31 | using HeuristicLab.Optimization.Operators;
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32 | using HeuristicLab.Parameters;
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33 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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34 | using HeuristicLab.Random;
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35 |
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36 | namespace HeuristicLab.Algorithms.LearningClassifierSystems {
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37 | /// <summary>
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38 | /// A learning classifier system.
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39 | /// </summary>
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40 | [Item("Learning Classifier System", "A genetic algorithm.")]
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41 | [Creatable("Algorithms")]
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42 | [StorableClass]
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43 | public sealed class LearningClassifierSystem : HeuristicOptimizationEngineAlgorithm, IStorableContent {
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44 | public string Filename { get; set; }
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45 |
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46 | #region Problem Properties
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47 | public override Type ProblemType {
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48 | get { return typeof(IConditionActionProblem); }
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49 | }
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50 | public new IConditionActionProblem Problem {
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51 | get { return (IConditionActionProblem)base.Problem; }
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52 | set { base.Problem = value; }
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53 | }
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54 | #endregion
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55 |
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56 | #region Parameter Properties
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57 | private ValueParameter<IntValue> SeedParameter {
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58 | get { return (ValueParameter<IntValue>)Parameters["Seed"]; }
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59 | }
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60 | private ValueParameter<BoolValue> SetSeedRandomlyParameter {
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61 | get { return (ValueParameter<BoolValue>)Parameters["SetSeedRandomly"]; }
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62 | }
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63 | private ValueParameter<BoolValue> CreateInitialPopulationParameter {
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64 | get { return (ValueParameter<BoolValue>)Parameters["CreateInitialPopulation"]; }
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65 | }
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66 | private ValueParameter<IntValue> PopulationSizeParameter {
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67 | get { return (ValueParameter<IntValue>)Parameters["N"]; }
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68 | }
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69 | private ValueParameter<PercentValue> BetaParameter {
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70 | get { return (ValueParameter<PercentValue>)Parameters["Beta"]; }
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71 | }
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72 | private ValueParameter<PercentValue> AlphaParameter {
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73 | get { return (ValueParameter<PercentValue>)Parameters["Alpha"]; }
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74 | }
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75 | private ValueParameter<DoubleValue> ErrorZeroParameter {
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76 | get { return (ValueParameter<DoubleValue>)Parameters["ErrorZero"]; }
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77 | }
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78 | private ValueParameter<DoubleValue> PowerParameter {
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79 | get { return (ValueParameter<DoubleValue>)Parameters["v"]; }
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80 | }
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81 | private ValueParameter<PercentValue> GammaParameter {
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82 | get { return (ValueParameter<PercentValue>)Parameters["Gamma"]; }
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83 | }
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84 | private ValueParameter<PercentValue> CrossoverProbabilityParameter {
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85 | get { return (ValueParameter<PercentValue>)Parameters["CrossoverProbability"]; }
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86 | }
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87 | private ValueParameter<PercentValue> MutationProbabilityParameter {
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88 | get { return (ValueParameter<PercentValue>)Parameters["MutationProbability"]; }
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89 | }
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90 | private ValueParameter<IntValue> ThetaGAParameter {
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91 | get { return (ValueParameter<IntValue>)Parameters["ThetaGA"]; }
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92 | }
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93 | private ValueParameter<IntValue> ThetaDeletionParameter {
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94 | get { return (ValueParameter<IntValue>)Parameters["ThetaDeletion"]; }
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95 | }
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96 | private ValueParameter<IntValue> ThetaSubsumptionParameter {
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97 | get { return (ValueParameter<IntValue>)Parameters["ThetaSubsumption"]; }
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98 | }
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99 | private ValueParameter<PercentValue> DeltaParameter {
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100 | get { return (ValueParameter<PercentValue>)Parameters["Delta"]; }
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101 | }
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102 | private ValueParameter<PercentValue> ExplorationProbabilityParameter {
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103 | get { return (ValueParameter<PercentValue>)Parameters["ExplorationProbability"]; }
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104 | }
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105 | private ValueParameter<BoolValue> DoGASubsumptionParameter {
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106 | get { return (ValueParameter<BoolValue>)Parameters["DoGASubsumption"]; }
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107 | }
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108 | private ValueParameter<BoolValue> DoActionSetSubsumptionParameter {
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109 | get { return (ValueParameter<BoolValue>)Parameters["DoActionSetSubsumption"]; }
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110 | }
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111 | private ValueParameter<MultiAnalyzer> AnalyzerParameter {
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112 | get { return (ValueParameter<MultiAnalyzer>)Parameters["Analyzer"]; }
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113 | }
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114 | private ValueParameter<MultiAnalyzer> FinalAnalyzerParameter {
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115 | get { return (ValueParameter<MultiAnalyzer>)Parameters["FinalAnalyzer"]; }
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116 | }
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117 | private ValueParameter<IntValue> MaxIterationsParameter {
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118 | get { return (ValueParameter<IntValue>)Parameters["MaxIterations"]; }
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119 | }
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120 | #endregion
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121 |
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122 | #region Properties
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123 | public IntValue Seed {
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124 | get { return SeedParameter.Value; }
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125 | set { SeedParameter.Value = value; }
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126 | }
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127 | public BoolValue SetSeedRandomly {
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128 | get { return SetSeedRandomlyParameter.Value; }
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129 | set { SetSeedRandomlyParameter.Value = value; }
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130 | }
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131 | public BoolValue CreateInitialPopulation {
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132 | get { return CreateInitialPopulationParameter.Value; }
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133 | set { CreateInitialPopulationParameter.Value = value; }
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134 | }
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135 | public IntValue PopulationSize {
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136 | get { return PopulationSizeParameter.Value; }
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137 | set { PopulationSizeParameter.Value = value; }
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138 | }
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139 | public PercentValue Beta {
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140 | get { return BetaParameter.Value; }
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141 | set { BetaParameter.Value = value; }
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142 | }
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143 | public PercentValue Alpha {
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144 | get { return AlphaParameter.Value; }
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145 | set { AlphaParameter.Value = value; }
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146 | }
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147 | public DoubleValue ErrorZero {
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148 | get { return ErrorZeroParameter.Value; }
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149 | set { ErrorZeroParameter.Value = value; }
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150 | }
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151 | public DoubleValue Power {
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152 | get { return PowerParameter.Value; }
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153 | set { PowerParameter.Value = value; }
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154 | }
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155 | public PercentValue Gamma {
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156 | get { return GammaParameter.Value; }
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157 | set { GammaParameter.Value = value; }
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158 | }
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159 | public PercentValue CrossoverProbability {
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160 | get { return CrossoverProbabilityParameter.Value; }
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161 | set { CrossoverProbabilityParameter.Value = value; }
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162 | }
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163 | public PercentValue MutationProbability {
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164 | get { return MutationProbabilityParameter.Value; }
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165 | set { MutationProbabilityParameter.Value = value; }
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166 | }
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167 | public IntValue ThetaGA {
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168 | get { return ThetaGAParameter.Value; }
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169 | set { ThetaGAParameter.Value = value; }
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170 | }
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171 | public IntValue ThetaDeletion {
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172 | get { return ThetaDeletionParameter.Value; }
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173 | set { ThetaDeletionParameter.Value = value; }
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174 | }
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175 | public IntValue ThetaSubsumption {
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176 | get { return ThetaSubsumptionParameter.Value; }
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177 | set { ThetaSubsumptionParameter.Value = value; }
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178 | }
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179 | public PercentValue Delta {
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180 | get { return DeltaParameter.Value; }
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181 | set { DeltaParameter.Value = value; }
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182 | }
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183 | public PercentValue ExplorationProbability {
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184 | get { return ExplorationProbabilityParameter.Value; }
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185 | set { ExplorationProbabilityParameter.Value = value; }
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186 | }
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187 | public BoolValue DoGASubsumption {
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188 | get { return DoGASubsumptionParameter.Value; }
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189 | set { DoGASubsumptionParameter.Value = value; }
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190 | }
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191 | public BoolValue DoActionSetSubsumption {
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192 | get { return DoActionSetSubsumptionParameter.Value; }
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193 | set { DoActionSetSubsumptionParameter.Value = value; }
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194 | }
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195 | public IntValue MaxIterations {
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196 | get { return MaxIterationsParameter.Value; }
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197 | set { MaxIterationsParameter.Value = value; }
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198 | }
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199 | public MultiAnalyzer Analyzer {
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200 | get { return AnalyzerParameter.Value; }
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201 | set { AnalyzerParameter.Value = value; }
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202 | }
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203 | public MultiAnalyzer FinalAnalyzer {
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204 | get { return FinalAnalyzerParameter.Value; }
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205 | set { FinalAnalyzerParameter.Value = value; }
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206 | }
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207 | private RandomCreator RandomCreator {
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208 | get { return (RandomCreator)OperatorGraph.InitialOperator; }
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209 | }
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210 | public LearningClassifierSystemMainLoop MainLoop {
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211 | get { return FindMainLoop(RandomCreator.Successor); }
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212 | }
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213 | #endregion
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214 |
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215 | public LearningClassifierSystem()
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216 | : base() {
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217 | #region Create parameters
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218 | Parameters.Add(new ValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
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219 | Parameters.Add(new ValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
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220 | Parameters.Add(new ValueParameter<BoolValue>("CreateInitialPopulation", "Specifies if a population should be created at the beginning of the algorithm.", new BoolValue(false)));
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221 | Parameters.Add(new ValueParameter<IntValue>("N", "Max size of the population of solutions.", new IntValue(100)));
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222 | Parameters.Add(new ValueParameter<PercentValue>("Beta", "Learning rate", new PercentValue(0.1)));
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223 | Parameters.Add(new ValueParameter<PercentValue>("Alpha", "", new PercentValue(0.1)));
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224 | Parameters.Add(new ValueParameter<DoubleValue>("ErrorZero", "The error below which classifiers are considered to have equal accuracy", new DoubleValue(10)));
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225 | Parameters.Add(new ValueParameter<DoubleValue>("v", "Power parameter", new DoubleValue(5)));
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226 | Parameters.Add(new ValueParameter<PercentValue>("Gamma", "Discount factor", new PercentValue(0.71)));
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227 | Parameters.Add(new ValueParameter<PercentValue>("CrossoverProbability", "Probability of crossover", new PercentValue(0.9)));
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228 | Parameters.Add(new ValueParameter<PercentValue>("MutationProbability", "Probability of mutation", new PercentValue(0.05)));
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229 | Parameters.Add(new ValueParameter<IntValue>("ThetaGA", "GA threshold. GA is applied in a set when the average time since the last GA is greater than ThetaGA.", new IntValue(25)));
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230 | Parameters.Add(new ValueParameter<IntValue>("ThetaDeletion", "Deletion threshold. If the experience of a classifier is greater than ThetaDeletion, its fitness may be considered in its probability of deletion.", new IntValue(20)));
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231 | Parameters.Add(new ValueParameter<IntValue>("ThetaSubsumption", "Subsumption threshold. The experience of a classifier must be greater than TheatSubsumption to be able to subsume another classifier.", new IntValue(20)));
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232 | Parameters.Add(new ValueParameter<PercentValue>("Delta", "Delta specifies the fraction of mean fitness in [P] below which the fitness of a classifier may be considered in its probability of deletion", new PercentValue(0.1)));
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233 | Parameters.Add(new ValueParameter<PercentValue>("ExplorationProbability", "Probability of selecting the action uniform randomly", new PercentValue(0.5)));
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234 | Parameters.Add(new ValueParameter<BoolValue>("DoGASubsumption", "Specifies if offsprings are tested for possible logical subsumption by parents.", new BoolValue(true)));
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235 | Parameters.Add(new ValueParameter<BoolValue>("DoActionSetSubsumption", "Specifies if action set is tested for subsuming classifiers.", new BoolValue(true)));
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236 | Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
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237 | Parameters.Add(new ValueParameter<MultiAnalyzer>("FinalAnalyzer", "The operator used to analyze the last generation.", new MultiAnalyzer()));
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238 | Parameters.Add(new ValueParameter<IntValue>("MaxIterations", "The maximum number of iterations.", new IntValue(1000)));
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239 | #endregion
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240 |
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241 | #region Create operators
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242 | RandomCreator randomCreator = new RandomCreator();
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243 |
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244 | ResultsCollector resultsCollector = new ResultsCollector();
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245 | LearningClassifierSystemMainLoop mainLoop = new LearningClassifierSystemMainLoop();
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246 |
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247 | randomCreator.RandomParameter.ActualName = "Random";
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248 | randomCreator.SeedParameter.ActualName = SeedParameter.Name;
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249 | randomCreator.SeedParameter.Value = null;
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250 | randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
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251 | randomCreator.SetSeedRandomlyParameter.Value = null;
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252 |
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253 | resultsCollector.ResultsParameter.ActualName = "Results";
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254 |
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255 | mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
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256 | mainLoop.FinalAnalyzerParameter.ActualName = FinalAnalyzerParameter.Name;
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257 | mainLoop.MaxIterationsParameter.ActualName = MaxIterationsParameter.Name;
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258 | #endregion
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259 |
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260 | #region Create operator graph
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261 | OperatorGraph.InitialOperator = randomCreator;
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262 | randomCreator.Successor = resultsCollector;
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263 | resultsCollector.Successor = mainLoop;
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264 | #endregion
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265 |
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266 | UpdateAnalyzers();
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267 | }
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268 | private LearningClassifierSystem(LearningClassifierSystem original, Cloner cloner)
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269 | : base(original, cloner) {
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270 | }
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271 | public override IDeepCloneable Clone(Cloner cloner) {
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272 | return new LearningClassifierSystem(this, cloner);
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273 | }
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274 | [StorableConstructor]
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275 | private LearningClassifierSystem(bool deserializing) : base(deserializing) { }
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276 |
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277 | protected override void OnProblemChanged() {
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278 | if (Problem != null) {
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279 | ParameterizeEvaluator(Problem.Evaluator);
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280 | MainLoop.SetCurrentProblem(Problem);
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281 | UpdateAnalyzers();
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282 | }
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283 | base.OnProblemChanged();
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284 | }
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285 | protected override void Problem_EvaluatorChanged(object sender, EventArgs e) {
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286 | ParameterizeEvaluator(Problem.Evaluator);
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287 | MainLoop.SetCurrentProblem(Problem);
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288 | base.Problem_EvaluatorChanged(sender, e);
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289 | }
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290 | protected override void Problem_SolutionCreatorChanged(object sender, EventArgs e) {
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291 | MainLoop.SetCurrentProblem(Problem);
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292 | base.Problem_SolutionCreatorChanged(sender, e);
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293 | }
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294 | protected override void Problem_OperatorsChanged(object sender, EventArgs e) {
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295 | UpdateAnalyzers();
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296 | base.Problem_OperatorsChanged(sender, e);
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297 | }
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298 |
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299 | private void ParameterizeEvaluator(IXCSEvaluator evaluator) {
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300 | evaluator.ActualTimeParameter.ActualName = "Iteration";
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301 | evaluator.BetaParameter.ActualName = BetaParameter.Name;
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302 | evaluator.AlphaParameter.ActualName = AlphaParameter.Name;
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303 | evaluator.PowerParameter.ActualName = PowerParameter.Name;
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304 | evaluator.ErrorZeroParameter.ActualName = ErrorZeroParameter.Name;
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305 | }
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306 |
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307 | private void UpdateAnalyzers() {
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308 | Analyzer.Operators.Clear();
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309 | FinalAnalyzer.Operators.Clear();
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310 | if (Problem != null) {
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311 | foreach (IAnalyzer analyzer in Problem.Operators.OfType<IAnalyzer>()) {
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312 | Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
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313 | FinalAnalyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
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314 | }
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315 | }
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316 | }
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317 |
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318 | private LearningClassifierSystemMainLoop FindMainLoop(IOperator start) {
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319 | IOperator mainLoop = start;
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320 | while (mainLoop != null && !(mainLoop is LearningClassifierSystemMainLoop))
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321 | mainLoop = ((SingleSuccessorOperator)mainLoop).Successor;
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322 | if (mainLoop == null) return null;
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323 | else return (LearningClassifierSystemMainLoop)mainLoop;
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324 | }
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325 | }
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326 | }
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