1 | using System;
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2 | using System.Collections.Generic;
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3 | using System.Linq;
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4 | using System.Threading;
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5 | using HEAL.Attic;
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6 | using HeuristicLab.Common;
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7 | using HeuristicLab.Core;
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8 | using HeuristicLab.Data;
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9 | using HeuristicLab.Encodings.RealVectorEncoding;
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10 | using HeuristicLab.Optimization;
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11 | using HeuristicLab.Parameters;
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12 | using HeuristicLab.Problems.TestFunctions.MultiObjective;
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13 | using HeuristicLab.Random;
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14 |
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15 | namespace HeuristicLab.Algorithms.NSGA3
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16 | {
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17 | /// <summary>
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18 | /// The Reference Point Based Non-dominated Sorting Genetic Algorithm III was introduced in Deb
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19 | /// et al. 2013. An Evolutionary Many-Objective Optimization Algorithm Using Reference Point
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20 | /// Based Non-dominated Sorting Approach. IEEE Transactions on Evolutionary Computation, 18(4),
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21 | /// pp. 577-601.
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22 | /// </summary>
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23 | [Item("NSGA-III", "The Reference Point Based Non-dominated Sorting Genetic Algorithm III was introduced in Deb et al. 2013. An Evolutionary Many-Objective Optimization Algorithm Using Reference Point Based Non-dominated Sorting Approach. IEEE Transactions on Evolutionary Computation, 18(4), pp. 577-601.")]
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24 | [Creatable(Category = CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 136)]
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25 | [StorableType("07C745F7-A8A3-4F99-8B2C-F97E639F9AC3")]
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26 | public class NSGA3 : BasicAlgorithm
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27 | {
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28 | public override bool SupportsPause => true;
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29 |
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30 | #region ProblemProperties
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31 |
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32 | public override Type ProblemType
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33 | {
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34 | get { return typeof(MultiObjectiveBasicProblem<RealVectorEncoding>); }
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35 | }
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36 |
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37 | public new MultiObjectiveBasicProblem<RealVectorEncoding> Problem
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38 | {
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39 | get { return (MultiObjectiveBasicProblem<RealVectorEncoding>)base.Problem; }
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40 | set { base.Problem = value; }
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41 | }
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42 |
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43 | public int Objectives
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44 | {
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45 | get
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46 | {
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47 | if (!(Problem is MultiObjectiveTestFunctionProblem testFunctionProblem)) throw new NotSupportedException("Only Multi Objective Test Function problems are supported");
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48 | return testFunctionProblem.Objectives;
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49 | }
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50 | }
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51 |
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52 | #endregion ProblemProperties
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53 |
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54 | #region Storable fields
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55 |
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56 | [Storable]
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57 | private IRandom random;
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58 |
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59 | [Storable]
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60 | private List<Solution> solutions; // maybe todo: rename to nextGeneration (see Run method)
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61 |
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62 | [Storable]
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63 | private List<ReferencePoint> referencePoints;
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64 |
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65 | #endregion Storable fields
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66 |
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67 | #region ParameterAndResultsNames
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68 |
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69 | // Parameter Names
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70 |
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71 | private const string PopulationSizeName = "Population Size";
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72 | private const string MaximumGenerationsName = "Maximum Generations";
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73 | private const string CrossoverProbabilityName = "Crossover Probability";
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74 | private const string MutationProbabilityName = "Mutation Probability";
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75 | private const string DominateOnEqualQualitiesName = "Dominate On Equal Qualities";
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76 | private const string SetSeedRandomlyName = "Set Seed Randomly";
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77 | private const string SeedName = "Seed";
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78 |
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79 | // Results Names
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80 |
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81 | private const string GeneratedReferencePointsResultName = "Generated Reference Points";
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82 | private const string CurrentGenerationResultName = "Generations";
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83 | private const string GenerationalDistanceResultName = "Generational Distance";
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84 | private const string InvertedGenerationalDistanceResultName = "Inverted Generational Distance";
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85 | private const string HypervolumeResultName = "Hypervolume";
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86 | private const string BestKnownHypervolumeResultName = "Best known hypervolume";
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87 | private const string DifferenceToBestKnownHypervolumeResultName = "Absolute Distance to BestKnownHypervolume";
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88 | private const string ScatterPlotResultName = "Scatter Plot";
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89 | private const string CurrentFrontResultName = "Pareto Front"; // Do not touch this
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90 |
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91 | #endregion ParameterAndResultsNames
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92 |
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93 | #region ParameterProperties
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94 |
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95 | private IFixedValueParameter<IntValue> PopulationSizeParameter
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96 | {
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97 | get { return (IFixedValueParameter<IntValue>)Parameters[PopulationSizeName]; }
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98 | }
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99 |
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100 | private IFixedValueParameter<IntValue> MaximumGenerationsParameter
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101 | {
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102 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumGenerationsName]; }
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103 | }
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104 |
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105 | private IFixedValueParameter<PercentValue> CrossoverProbabilityParameter
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106 | {
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107 | get { return (IFixedValueParameter<PercentValue>)Parameters[CrossoverProbabilityName]; }
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108 | }
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109 |
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110 | private IFixedValueParameter<PercentValue> MutationProbabilityParameter
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111 | {
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112 | get { return (IFixedValueParameter<PercentValue>)Parameters[MutationProbabilityName]; }
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113 | }
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114 |
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115 | private IFixedValueParameter<BoolValue> DominateOnEqualQualitiesParameter
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116 | {
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117 | get { return (IFixedValueParameter<BoolValue>)Parameters[DominateOnEqualQualitiesName]; }
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118 | }
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119 |
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120 | private IFixedValueParameter<BoolValue> SetSeedRandomlyParameter
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121 | {
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122 | get { return (IFixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyName]; }
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123 | }
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124 |
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125 | private IFixedValueParameter<IntValue> SeedParameter
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126 | {
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127 | get { return (IFixedValueParameter<IntValue>)Parameters[SeedName]; }
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128 | }
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129 |
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130 | #endregion ParameterProperties
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131 |
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132 | #region Properties
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133 |
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134 | public IntValue PopulationSize => PopulationSizeParameter.Value;
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135 |
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136 | public IntValue MaximumGenerations => MaximumGenerationsParameter.Value;
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137 |
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138 | public PercentValue CrossoverProbability => CrossoverProbabilityParameter.Value;
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139 |
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140 | public PercentValue MutationProbability => MutationProbabilityParameter.Value;
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141 |
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142 | public BoolValue DominateOnEqualQualities => DominateOnEqualQualitiesParameter.Value;
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143 |
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144 | public BoolValue SetSeedRandomly => SetSeedRandomlyParameter.Value;
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145 | public IntValue Seed => SeedParameter.Value;
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146 |
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147 | // todo: create one property for the Generated Reference Points and one for the current
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148 | // generations reference points
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149 |
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150 | #endregion Properties
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151 |
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152 | #region ResultsProperties
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153 |
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154 | public DoubleMatrix ResultsGeneratedReferencePoints
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155 | {
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156 | get { return (DoubleMatrix)Results[GeneratedReferencePointsResultName].Value; }
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157 | set { Results[GeneratedReferencePointsResultName].Value = value; }
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158 | }
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159 |
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160 | public IntValue ResultsCurrentGeneration
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161 | {
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162 | get { return (IntValue)Results[CurrentGenerationResultName].Value; }
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163 | set { Results[CurrentGenerationResultName].Value = value; }
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164 | }
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165 |
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166 | public DoubleValue ResultsGenerationalDistance
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167 | {
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168 | get { return (DoubleValue)Results[GenerationalDistanceResultName].Value; }
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169 | set { Results[GenerationalDistanceResultName].Value = value; }
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170 | }
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171 |
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172 | public DoubleValue ResultsInvertedGenerationalDistance
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173 | {
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174 | get { return (DoubleValue)Results[InvertedGenerationalDistanceResultName].Value; }
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175 | set { Results[InvertedGenerationalDistanceResultName].Value = value; }
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176 | }
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177 |
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178 | public DoubleValue ResultsHypervolume
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179 | {
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180 | get { return (DoubleValue)Results[HypervolumeResultName].Value; }
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181 | set { Results[HypervolumeResultName].Value = value; }
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182 | }
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183 |
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184 | public DoubleValue ResultsBestKnownHypervolume
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185 | {
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186 | get { return (DoubleValue)Results[BestKnownHypervolumeResultName].Value; }
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187 | set { Results[BestKnownHypervolumeResultName].Value = value; }
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188 | }
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189 |
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190 | public DoubleValue ResultsDifferenceToBestKnownHypervolume
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191 | {
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192 | get { return (DoubleValue)Results[DifferenceToBestKnownHypervolumeResultName].Value; }
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193 | set { Results[DifferenceToBestKnownHypervolumeResultName].Value = value; }
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194 | }
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195 |
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196 | public ParetoFrontScatterPlot ResultsScatterPlot
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197 | {
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198 | get { return (ParetoFrontScatterPlot)Results[ScatterPlotResultName].Value; }
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199 | set { Results[ScatterPlotResultName].Value = value; }
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200 | }
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201 |
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202 | public DoubleMatrix ResultsSolutions
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203 | {
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204 | get { return (DoubleMatrix)Results[CurrentFrontResultName].Value; }
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205 | set { Results[CurrentFrontResultName].Value = value; }
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206 | }
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207 |
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208 | #endregion ResultsProperties
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209 |
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210 | #region Constructors
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211 |
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212 | public NSGA3() : base()
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213 | {
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214 | Parameters.Add(new FixedValueParameter<IntValue>(PopulationSizeName, "The size of the population of Individuals.", new IntValue(200)));
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215 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumGenerationsName, "The maximum number of generations which should be processed.", new IntValue(1000)));
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216 | Parameters.Add(new FixedValueParameter<PercentValue>(CrossoverProbabilityName, "The probability that the crossover operator is applied on two parents.", new PercentValue(1.0)));
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217 | Parameters.Add(new FixedValueParameter<PercentValue>(MutationProbabilityName, "The probability that the mutation operator is applied on a Individual.", new PercentValue(0.05)));
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218 | Parameters.Add(new FixedValueParameter<BoolValue>(DominateOnEqualQualitiesName, "Flag which determines wether Individuals with equal quality values should be treated as dominated.", new BoolValue(false)));
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219 | Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyName, "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 FixedValueParameter<IntValue>(SeedName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
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221 | }
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222 |
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223 | // Persistence uses this ctor to improve deserialization efficiency. If we would use the
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224 | // default ctor instead this would completely initialize the object (e.g. creating
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225 | // parameters) even though the data is later overwritten by the stored data.
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226 | [StorableConstructor]
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227 | public NSGA3(StorableConstructorFlag _) : base(_) { }
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228 |
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229 | // Each clonable item must have a cloning ctor (deep cloning, the cloner is used to handle
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230 | // cyclic object references). Don't forget to call the cloning ctor of the base class
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231 | public NSGA3(NSGA3 original, Cloner cloner) : base(original, cloner)
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232 | {
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233 | random = cloner.Clone(original.random);
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234 | solutions = original.solutions?.Select(cloner.Clone).ToList();
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235 | referencePoints = original.referencePoints?.Select(cloner.Clone).ToList();
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236 | }
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237 |
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238 | public override IDeepCloneable Clone(Cloner cloner)
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239 | {
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240 | return new NSGA3(this, cloner);
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241 | }
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242 |
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243 | #endregion Constructors
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244 |
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245 | #region Initialization
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246 |
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247 | protected override void Initialize(CancellationToken cancellationToken)
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248 | {
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249 | base.Initialize(cancellationToken);
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250 |
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251 | SetParameters();
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252 | InitResults();
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253 | InitFields();
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254 | Analyze();
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255 | }
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256 |
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257 | // todo: trigger this when the problem parameters change
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258 | private void SetParameters()
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259 | {
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260 | // Set population size
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261 | int numberOfGeneratedReferencePoints = ReferencePoint.GetNumberOfGeneratedReferencePoints(Objectives);
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262 | if (numberOfGeneratedReferencePoints == -1) throw new NotSupportedException("The number of objectives is not supported.");
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263 | PopulationSize.Value = ReferencePoint.GetPopulationSizeForReferencePoints(numberOfGeneratedReferencePoints);
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264 |
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265 | // Set mutation probability
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266 | MutationProbability.Value = 1.0 / Problem.Encoding.Length;
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267 |
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268 | // todo: Set MaximumGenerations in Problem definition
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269 | }
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270 |
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271 | private void InitResults()
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272 | {
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273 | Results.Add(new Result(GeneratedReferencePointsResultName, "The initially generated reference points", new DoubleMatrix()));
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274 | Results.Add(new Result(CurrentGenerationResultName, "The current generation", new IntValue(1)));
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275 | Results.Add(new Result(GenerationalDistanceResultName, "The generational distance to an optimal pareto front defined in the Problem", new DoubleValue(double.NaN)));
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276 | Results.Add(new Result(InvertedGenerationalDistanceResultName, "The inverted generational distance to an optimal pareto front defined in the Problem", new DoubleValue(double.NaN)));
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277 | Results.Add(new Result(HypervolumeResultName, "The hypervolume of the current front considering the Reference point defined in the Problem", new DoubleValue(double.NaN)));
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278 | Results.Add(new Result(BestKnownHypervolumeResultName, "The best known hypervolume considering the Reference point defined in the Problem", new DoubleValue(double.NaN)));
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279 | Results.Add(new Result(DifferenceToBestKnownHypervolumeResultName, "The difference between the current and the best known hypervolume", new DoubleValue(double.NaN)));
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280 | Results.Add(new Result(ScatterPlotResultName, "A scatterplot displaying the evaluated solutions and (if available) the analytically optimal front", new ParetoFrontScatterPlot()));
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281 | Results.Add(new Result(CurrentFrontResultName, "The Pareto Front", new DoubleMatrix()));
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282 |
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283 | if (!(Problem is MultiObjectiveTestFunctionProblem problem)) return;
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284 | // todo: add BestKnownFront parameter
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285 | ResultsScatterPlot = new ParetoFrontScatterPlot(new double[0][], new double[0][], problem.BestKnownFront.ToJaggedArray(), problem.Objectives, problem.ProblemSize);
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286 | if (problem.BestKnownFront == null) return;
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287 | ResultsBestKnownHypervolume = new DoubleValue(Hypervolume.Calculate(problem.BestKnownFront.ToJaggedArray(), problem.ReferencePoint.CloneAsArray(), problem.Maximization));
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288 | }
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289 |
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290 | private void InitFields()
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291 | {
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292 | random = new MersenneTwister();
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293 | if (!SetSeedRandomly.Value)
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294 | random.Reset(Seed.Value);
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295 |
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296 | solutions = GetInitialPopulation();
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297 | referencePoints = ReferencePoint.GenerateReferencePoints(random, Objectives);
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298 | ResultsGeneratedReferencePoints = Utility.ConvertToDoubleMatrix(referencePoints);
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299 | }
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300 |
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301 | private List<Solution> GetInitialPopulation()
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302 | {
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303 | var problem = Problem as MultiObjectiveTestFunctionProblem;
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304 | if (problem.Bounds.Rows != 1) throw new Exception();
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305 |
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306 | // Initialise solutions
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307 | List<Solution> solutions = new List<Solution>(PopulationSize.Value);
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308 | for (int i = 0; i < PopulationSize.Value; i++)
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309 | {
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310 | double minBound = problem.Bounds[0, 0];
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311 | double maxBound = problem.Bounds[0, 1];
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312 | RealVector randomRealVector = new RealVector(Problem.Encoding.Length, random, minBound, maxBound);
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313 | var solution = new Solution(randomRealVector);
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314 | solution.Fitness = Evaluate(solution.Chromosome);
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315 | solutions.Add(solution);
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316 | }
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317 |
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318 | return solutions;
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319 | }
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320 |
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321 | #endregion Initialization
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322 |
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323 | #region Overriden Methods
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324 |
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325 | protected override void Run(CancellationToken cancellationToken)
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326 | {
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327 | try
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328 | {
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329 | while (ResultsCurrentGeneration.Value < MaximumGenerations.Value)
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330 | {
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331 | // todo: make parameter out of this
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332 | List<Solution> qt = Mutate(Recombine(solutions), 30);
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333 | foreach (var solution in qt)
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334 | solution.Fitness = Evaluate(solution.Chromosome);
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335 |
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336 | List<Solution> rt = Utility.Concat(solutions, qt);
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337 |
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338 | // todo: remove this check
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339 | for (int i = 0; i < rt.Count / 2; i++)
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340 | if (!solutions.Contains(rt[i])) throw new Exception($"This should never happen: !solutions.Contains(rt[{i}])");
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341 |
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342 | solutions = NSGA3Selection.SelectSolutionsForNextGeneration(rt, GetCopyOfReferencePoints(), Problem.Maximization, PopulationSize.Value, random);
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343 |
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344 | ResultsCurrentGeneration.Value++;
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345 | cancellationToken.ThrowIfCancellationRequested();
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346 | }
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347 | }
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348 | catch (OperationCanceledException ex)
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349 | {
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350 | throw new OperationCanceledException("Optimization process was cancelled.", ex);
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351 | }
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352 | catch (Exception ex)
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353 | {
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354 | throw new Exception($"Failed in generation {ResultsCurrentGeneration}.", ex);
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355 | }
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356 | finally
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357 | {
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358 | Analyze();
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359 | }
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360 | }
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361 |
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362 | #endregion Overriden Methods
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363 |
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364 | #region Private Methods
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365 |
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366 | private List<ReferencePoint> GetCopyOfReferencePoints()
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367 | {
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368 | if (referencePoints == null) return null;
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369 |
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370 | return referencePoints.Select(rp => (ReferencePoint)rp.Clone()).ToList();
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371 | }
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372 |
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373 | private void Analyze()
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374 | {
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375 | ResultsScatterPlot = new ParetoFrontScatterPlot(solutions.Select(x => x.Fitness).ToArray(), solutions.Select(x => x.Chromosome.ToArray()).ToArray(), ResultsScatterPlot.ParetoFront, ResultsScatterPlot.Objectives, ResultsScatterPlot.ProblemSize);
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376 | ResultsSolutions = solutions.Select(s => s.Chromosome.ToArray()).ToMatrix();
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377 |
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378 | if (!(Problem is MultiObjectiveTestFunctionProblem problem)) return;
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379 |
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380 | // Indicators
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381 | ResultsGenerationalDistance = new DoubleValue(problem.BestKnownFront != null ? GenerationalDistance.Calculate(solutions.Select(s => s.Fitness), problem.BestKnownFront.ToJaggedArray(), 1) : double.NaN);
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382 | ResultsInvertedGenerationalDistance = new DoubleValue(problem.BestKnownFront != null ? InvertedGenerationalDistance.Calculate(solutions.Select(s => s.Fitness), problem.BestKnownFront.ToJaggedArray(), 1) : double.NaN);
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383 |
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384 | var front = NonDominatedSelect.GetDominatingVectors(solutions.Select(x => x.Fitness), problem.ReferencePoint.CloneAsArray(), Problem.Maximization, true).ToArray();
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385 | if (front.Length == 0) return;
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386 | ResultsHypervolume = new DoubleValue(Hypervolume.Calculate(front, problem.ReferencePoint.CloneAsArray(), problem.Maximization));
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387 | ResultsDifferenceToBestKnownHypervolume = new DoubleValue(ResultsBestKnownHypervolume.Value - ResultsHypervolume.Value);
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388 | }
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389 |
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390 | /// <summary>
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391 | /// Returns the fitness of the given <paramref name="chromosome" /> by applying the Evaluate
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392 | /// method of the Problem.
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393 | /// </summary>
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394 | /// <param name="chromosome"></param>
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395 | /// <returns></returns>
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396 | private double[] Evaluate(RealVector chromosome)
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397 | {
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398 | return Problem.Evaluate(new SingleEncodingIndividual(Problem.Encoding, new Scope { Variables = { new Variable(Problem.Encoding.Name, chromosome) } }), random);
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399 | }
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400 |
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401 | private List<Solution> Recombine(List<Solution> solutions)
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402 | {
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403 | List<Solution> childSolutions = new List<Solution>();
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404 |
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405 | for (int i = 0; i < solutions.Count; i += 2)
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406 | {
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407 | int parentIndex1 = random.Next(solutions.Count);
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408 | int parentIndex2 = random.Next(solutions.Count);
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409 | // ensure that the parents are not the same object
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410 | if (parentIndex1 == parentIndex2) parentIndex2 = (parentIndex2 + 1) % solutions.Count;
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411 | var parent1 = solutions[parentIndex1];
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412 | var parent2 = solutions[parentIndex2];
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413 |
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414 | // Do crossover with crossoverProbabilty == 1 in order to guarantee that a crossover happens
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415 | var children = SimulatedBinaryCrossover.Apply(random,
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416 | Problem.Encoding.Bounds, parent1.Chromosome, parent2.Chromosome, CrossoverProbability.Value);
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417 |
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418 | childSolutions.Add(new Solution(children.Item1));
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419 | childSolutions.Add(new Solution(children.Item2));
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420 | }
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421 |
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422 | return childSolutions;
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423 | }
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424 |
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425 | private List<Solution> Mutate(List<Solution> solutions, double eta)
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426 | {
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427 | foreach (var solution in solutions)
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428 | {
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429 | for (int i = 0; i < solution.Chromosome.Length; i++)
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430 | {
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431 | if (random.NextDouble() > MutationProbability.Value) continue;
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432 |
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433 | double y = solution.Chromosome[i];
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434 | double lb;
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435 | double ub;
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436 | var problem = Problem as MultiObjectiveTestFunctionProblem;
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437 | if (problem.Bounds.Rows == 1) lb = problem.Bounds[0, 0];
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438 | else lb = problem.Bounds[i, 0];
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439 | if (problem.Bounds.Rows == 1) ub = problem.Bounds[0, 1];
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440 | else ub = problem.Bounds[i, 1];
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441 |
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442 | double delta1 = (y - lb) / (ub - lb);
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443 | double delta2 = (ub - y) / (ub - lb);
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444 |
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445 | double mut_pow = 1.0 / (eta + 1.0);
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446 |
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447 | double rnd = random.NextDouble();
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448 | double deltaq;
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449 | if (rnd <= 0.5)
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450 | {
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451 | double xy = 1.0 - delta1;
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452 | double val = 2.0 * rnd + (1.0 - 2.0 * rnd) * (Math.Pow(xy, (eta + 1.0)));
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453 | deltaq = Math.Pow(val, mut_pow) - 1.0;
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454 | }
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455 | else
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456 | {
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457 | double xy = 1.0 - delta2;
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458 | double val = 2.0 * (1.0 - rnd) + 2.0 * (rnd - 0.5) * (Math.Pow(xy, (eta + 1.0)));
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459 | deltaq = 1.0 - (Math.Pow(val, mut_pow));
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460 | }
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461 |
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462 | y += deltaq * (ub - lb);
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463 | y = Math.Min(ub, Math.Max(lb, y));
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464 |
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465 | solution.Chromosome[i] = y;
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466 | }
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467 | }
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468 | return solutions;
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469 | }
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470 |
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471 | #endregion Private Methods
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472 | }
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473 | } |
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