1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2019 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 | // 03/02/2020
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23 | // TODO LIST:
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24 | // 1. Dynamic reference point strategy
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25 | // 2. Normalized fitness value strategy, desibility function.
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26 | // 3. HVC calculation should be definitely improved, at least in the 2D case.
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27 | // 4. multiple point strategy when $\lambda>1$
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28 |
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29 | using HEAL.Attic;
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30 | using HeuristicLab.Analysis;
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31 | using HeuristicLab.Common;
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32 | using HeuristicLab.Core;
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33 | using HeuristicLab.Data;
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34 | using HeuristicLab.ExpressionGenerator;
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35 | using HeuristicLab.Optimization;
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36 | using HeuristicLab.Parameters;
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37 | using HeuristicLab.Problems.DataAnalysis;
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38 | using HeuristicLab.Problems.TestFunctions.MultiObjective;
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39 | using HeuristicLab.Random;
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40 | using System;
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41 | using System.Collections.Generic;
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42 | using System.Drawing;
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43 | using System.Linq;
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44 | using CancellationToken = System.Threading.CancellationToken;
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45 |
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46 | namespace HeuristicLab.Algorithms.DynamicALPS {
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47 | [Item("DynamicALPSAlgorithmBase", "Base class for all DynamicALPS algorithm variants.")]
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48 | [StorableType("C0141748-DF5A-4CA0-A3DD-1DFB98236F7E")]
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49 | public abstract class DynamicALPSAlgorithmBase : BasicAlgorithm {
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50 | #region data members
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51 |
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52 | [StorableType("75C9EA99-D699-4A1F-8AB2-AB86B7A2FD68")]
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53 | protected enum NeighborType { NEIGHBOR, POPULATION }
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54 |
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55 |
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56 | [StorableType("2A71E397-05CE-460F-B982-EE2F4B37C354")]
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57 | // TCHE = Chebyshev (Tchebyshev)
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58 | // PBI = Penalty-based boundary intersection
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59 | // AGG = Weighted sum
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60 | public enum FunctionType { TCHE, PBI, AGG }
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61 |
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62 | [Storable]
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63 | protected double[] IdealPoint { get; set; }
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64 | [Storable]
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65 | protected double[] NadirPoint { get; set; } // potentially useful for objective normalization
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66 |
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67 | [Storable]
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68 | protected double[][] lambda_moead;
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69 |
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70 | [Storable]
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71 | protected int[][] neighbourhood;
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72 |
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73 | [Storable]
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74 | protected IDynamicALPSSolution[] solutions;
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75 |
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76 | [Storable]
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77 | protected FunctionType functionType;
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78 |
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79 | [Storable]
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80 | protected IDynamicALPSSolution[] population;
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81 |
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82 | [Storable]
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83 | protected IDynamicALPSSolution[][] layerPopulation;
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84 |
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85 | [Storable]
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86 | protected IDynamicALPSSolution[][] layerOffspringPopulation;
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87 |
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88 | [Storable]
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89 | protected IDynamicALPSSolution[][] layerJointPopulation;
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90 |
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91 | [Storable]
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92 | protected IDynamicALPSSolution[] offspringPopulation;
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93 |
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94 | //[Storable]
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95 | //protected IDynamicALPSSolution[] jointPopulation;
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96 |
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97 | [Storable]
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98 | protected int evaluatedSolutions;
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99 |
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100 | [Storable]
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101 | protected ExecutionContext executionContext;
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102 |
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103 | [Storable]
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104 | protected IScope globalScope;
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105 |
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106 | [Storable]
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107 | protected ExecutionState previousExecutionState;
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108 |
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109 | [Storable]
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110 | protected ExecutionState executionState;
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111 |
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112 | private DoubleArray ReferencePoint {
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113 | get {
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114 | var problem = (MultiObjectiveTestFunctionProblem)Problem;
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115 | return problem.ReferencePoint;
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116 | }
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117 | }
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118 | #endregion
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119 |
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120 | #region parameters
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121 | private const string SeedParameterName = "Seed";
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122 | private const string SetSeedRandomlyParameterName = "SetSeedRandomly";
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123 | private const string PopulationSizeParameterName = "PopulationSize";
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124 | private const string ResultPopulationSizeParameterName = "ResultPopulationSize";
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125 | private const string CrossoverProbabilityParameterName = "CrossoverProbability";
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126 | private const string CrossoverParameterName = "Crossover";
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127 | private const string MutationProbabilityParameterName = "MutationProbability";
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128 | private const string MutatorParameterName = "Mutator";
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129 | private const string MaximumEvaluatedSolutionsParameterName = "MaximumEvaluatedSolutions";
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130 | private const string RandomParameterName = "Random";
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131 | private const string AnalyzerParameterName = "Analyzer";
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132 | // MOEA-D parameters
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133 | //private const string NeighbourSizeParameterName = "NeighbourSize";
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134 | //private const string NeighbourhoodSelectionProbabilityParameterName = "NeighbourhoodSelectionProbability";
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135 | //private const string MaximumNumberOfReplacedSolutionsParameterName = "MaximumNumberOfReplacedSolutions";
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136 | //private const string FunctionTypeParameterName = "FunctionType";
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137 | // private const string NormalizeObjectivesParameterName = "NormalizeObjectives";
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138 |
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139 | // SMS-EMOA parameters:
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140 | private const string LambdaParameterName = "Lambda"; // The number of offspring size
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141 | private const string ALPSLayersParameterName = "ALPSLayers"; // The number of offspring size
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142 | private const string ALPSAgeGapParameterName = "ALPSAgeGap"; // The number of offspring size
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143 |
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144 |
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145 |
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146 | // "Parameters" are defined in "HeuristicLab.Parameters"
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147 | // Contains: generic parameters of every class/algorithm/instance,
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148 | // It seems that "I***ValueParameter" is declared in "Heuristic.core", where "***ValueParameter" are defined in "HeuristicLab.Parameter"
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149 | // The function of "I***ValueParameter" is to bridge current scripts to "HeuristicLab.Parameter".
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150 | public IValueParameter<MultiAnalyzer> AnalyzerParameter {
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151 | get { return (ValueParameter<MultiAnalyzer>)Parameters[AnalyzerParameterName]; }
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152 | }
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153 |
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154 | //public IConstrainedValueParameter<StringValue> FunctionTypeParameter
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155 | //{
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156 | // get { return (IConstrainedValueParameter<StringValue>)Parameters[FunctionTypeParameterName]; }
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157 | //}
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158 | //public IFixedValueParameter<IntValue> NeighbourSizeParameter
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159 | //{
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160 | // get { return (IFixedValueParameter<IntValue>)Parameters[NeighbourSizeParameterName]; }
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161 | //}
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162 | //public IFixedValueParameter<BoolValue> NormalizeObjectivesParameter
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163 | //{
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164 | // get { return (IFixedValueParameter<BoolValue>)Parameters[NormalizeObjectivesParameterName]; }
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165 | //}
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166 | //public IFixedValueParameter<IntValue> MaximumNumberOfReplacedSolutionsParameter
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167 | //{
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168 | // get { return (IFixedValueParameter<IntValue>)Parameters[MaximumNumberOfReplacedSolutionsParameterName]; }
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169 | //}
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170 | //public IFixedValueParameter<DoubleValue> NeighbourhoodSelectionProbabilityParameter
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171 | //{
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172 | // get { return (IFixedValueParameter<DoubleValue>)Parameters[NeighbourhoodSelectionProbabilityParameterName]; }
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173 | //}
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174 | public IFixedValueParameter<IntValue> SeedParameter {
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175 | get { return (IFixedValueParameter<IntValue>)Parameters[SeedParameterName]; }
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176 | }
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177 | public IFixedValueParameter<BoolValue> SetSeedRandomlyParameter {
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178 | get { return (IFixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyParameterName]; }
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179 | }
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180 | private IValueParameter<IntValue> PopulationSizeParameter {
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181 | get { return (IValueParameter<IntValue>)Parameters[PopulationSizeParameterName]; }
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182 | }
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183 | // KF, SMS-EMOA
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184 | private IValueParameter<IntValue> LambdaParameter {
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185 | get { return (IValueParameter<IntValue>)Parameters[LambdaParameterName]; }
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186 | }
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187 | //// KF, DynamicALPS
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188 | private IValueParameter<IntValue> ALPSLayersParameter{
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189 | get { return (IValueParameter<IntValue>)Parameters[ALPSLayersParameterName]; }
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190 | }
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191 | private IValueParameter<IntValue> ALPSAgeGapParameter {
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192 | get { return (IValueParameter<IntValue>)Parameters[ALPSAgeGapParameterName]; }
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193 | }
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194 |
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195 | private IValueParameter<IntValue> ResultPopulationSizeParameter {
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196 | get { return (IValueParameter<IntValue>)Parameters[ResultPopulationSizeParameterName]; }
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197 | }
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198 |
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199 | public IValueParameter<PercentValue> CrossoverProbabilityParameter {
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200 | get { return (IValueParameter<PercentValue>)Parameters[CrossoverProbabilityParameterName]; }
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201 | }
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202 | public IConstrainedValueParameter<ICrossover> CrossoverParameter {
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203 | get { return (IConstrainedValueParameter<ICrossover>)Parameters[CrossoverParameterName]; }
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204 | }
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205 | public IValueParameter<PercentValue> MutationProbabilityParameter {
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206 | get { return (IValueParameter<PercentValue>)Parameters[MutationProbabilityParameterName]; }
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207 | }
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208 | public IConstrainedValueParameter<IManipulator> MutatorParameter {
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209 | get { return (IConstrainedValueParameter<IManipulator>)Parameters[MutatorParameterName]; }
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210 | }
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211 | public IValueParameter<IntValue> MaximumEvaluatedSolutionsParameter {
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212 | get { return (IValueParameter<IntValue>)Parameters[MaximumEvaluatedSolutionsParameterName]; }
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213 | }
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214 | public IValueParameter<IRandom> RandomParameter {
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215 | get { return (IValueParameter<IRandom>)Parameters[RandomParameterName]; }
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216 | }
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217 | #endregion
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218 |
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219 | #region parameter properties
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220 | public new IMultiObjectiveHeuristicOptimizationProblem Problem {
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221 | get { return (IMultiObjectiveHeuristicOptimizationProblem)base.Problem; }
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222 | set { base.Problem = value; }
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223 | }
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224 | public int Seed {
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225 | get { return SeedParameter.Value.Value; }
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226 | set { SeedParameter.Value.Value = value; }
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227 | }
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228 | public bool SetSeedRandomly {
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229 | get { return SetSeedRandomlyParameter.Value.Value; }
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230 | set { SetSeedRandomlyParameter.Value.Value = value; }
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231 | }
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232 | public IntValue PopulationSize {
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233 | get { return PopulationSizeParameter.Value; }
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234 | set { PopulationSizeParameter.Value = value; }
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235 | }
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236 | public IntValue Lambda {
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237 | get { return LambdaParameter.Value; }
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238 | set { LambdaParameter.Value = value; }
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239 | }
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240 |
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241 | public IntValue ResultPopulationSize {
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242 | get { return ResultPopulationSizeParameter.Value; }
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243 | set { ResultPopulationSizeParameter.Value = value; }
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244 | }
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245 |
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246 | public IntValue ALPSLayers {
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247 | get { return ALPSLayersParameter.Value; }
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248 | set { ALPSLayersParameter.Value = value; }
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249 | }
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250 |
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251 | public IntValue ALPSAgeGap {
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252 | get { return ALPSAgeGapParameter.Value; }
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253 | set { ALPSAgeGapParameter.Value = value; }
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254 | }
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255 |
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256 | public PercentValue CrossoverProbability {
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257 | get { return CrossoverProbabilityParameter.Value; }
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258 | set { CrossoverProbabilityParameter.Value = value; }
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259 | }
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260 | public ICrossover Crossover {
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261 | get { return CrossoverParameter.Value; }
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262 | set { CrossoverParameter.Value = value; }
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263 | }
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264 | public PercentValue MutationProbability {
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265 | get { return MutationProbabilityParameter.Value; }
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266 | set { MutationProbabilityParameter.Value = value; }
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267 | }
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268 | public IManipulator Mutator {
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269 | get { return MutatorParameter.Value; }
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270 | set { MutatorParameter.Value = value; }
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271 | }
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272 | public MultiAnalyzer Analyzer {
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273 | get { return AnalyzerParameter.Value; }
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274 | set { AnalyzerParameter.Value = value; }
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275 | }
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276 | public IntValue MaximumEvaluatedSolutions {
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277 | get { return MaximumEvaluatedSolutionsParameter.Value; }
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278 | set { MaximumEvaluatedSolutionsParameter.Value = value; }
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279 | }
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280 | #endregion
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281 |
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282 | #region constructors
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283 | public DynamicALPSAlgorithmBase() {
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284 | // Add or define or specify the parameters that may be use in SMS-EMOA.
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285 | // ***("Name", "Description", "Value")
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286 | // Name Type Description
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287 | // FixedValueParameter: ANY Not changed???
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288 | // ValueParameter: Changable??? What is the difference between "ValueParameter" and "FixedVlaueParameter"?????
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289 |
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290 |
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291 | // types:
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292 | // IntValue
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293 | // BoolValue
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294 | // DoubleValue
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295 | // PercentValue
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296 | // ICrossover:
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297 | // IManipulator:
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298 | // IRandom:
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299 | // MultiAnalyzer:
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300 | // ---------
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301 | Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
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302 | Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
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303 | Parameters.Add(new ValueParameter<IntValue>(PopulationSizeParameterName, "The size of the population of solutions.", new IntValue(100)));
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304 | Parameters.Add(new ValueParameter<IntValue>(ResultPopulationSizeParameterName, "The size of the population of solutions.", new IntValue(100)));
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305 | Parameters.Add(new ValueParameter<PercentValue>(CrossoverProbabilityParameterName, "The probability that the crossover operator is applied.", new PercentValue(0.9)));
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306 | Parameters.Add(new ConstrainedValueParameter<ICrossover>(CrossoverParameterName, "The operator used to cross solutions."));
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307 | Parameters.Add(new ValueParameter<PercentValue>(MutationProbabilityParameterName, "The probability that the mutation operator is applied on a solution.", new PercentValue(0.25)));
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308 | Parameters.Add(new ConstrainedValueParameter<IManipulator>(MutatorParameterName, "The operator used to mutate solutions."));
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309 | Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
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310 | Parameters.Add(new ValueParameter<IntValue>(MaximumEvaluatedSolutionsParameterName, "The maximum number of evaluated solutions (approximately).", new IntValue(100_000)));
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311 | Parameters.Add(new ValueParameter<IRandom>(RandomParameterName, new FastRandom()));
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312 |
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313 | // SMS-EMOA, kf
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314 | Parameters.Add(new ValueParameter<IntValue>(LambdaParameterName, "The size of the offsprings. Now, it only works when lambda = 1", new IntValue(1)));
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315 | // DynamicALPS, KF
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316 | Parameters.Add(new ValueParameter<IntValue>(ALPSLayersParameterName, "Test, maximum = 1000, defualt is 1", new IntValue(10)));
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317 | Parameters.Add(new ValueParameter<IntValue>(ALPSAgeGapParameterName, "Test, maximum = 1000, defualt is 20", new IntValue(20)));
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318 |
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319 |
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320 | }
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321 |
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322 | protected DynamicALPSAlgorithmBase(DynamicALPSAlgorithmBase original, Cloner cloner) : base(original, cloner) {
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323 | functionType = original.functionType;
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324 | evaluatedSolutions = original.evaluatedSolutions;
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325 | previousExecutionState = original.previousExecutionState;
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326 |
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327 | if (original.IdealPoint != null) {
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328 | IdealPoint = (double[])original.IdealPoint.Clone();
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329 | }
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330 |
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331 | if (original.NadirPoint != null) {
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332 | NadirPoint = (double[])original.NadirPoint.Clone();
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333 | }
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334 |
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335 | if (original.lambda_moead != null) {
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336 | lambda_moead = (double[][])original.lambda_moead.Clone();
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337 | }
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338 |
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339 | if (original.neighbourhood != null) {
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340 | neighbourhood = (int[][])original.neighbourhood.Clone();
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341 | }
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342 |
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343 | if (original.solutions != null) {
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344 | solutions = original.solutions.Select(cloner.Clone).ToArray();
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345 | }
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346 |
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347 | if (original.population != null) {
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348 | population = original.population.Select(cloner.Clone).ToArray();
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349 | }
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350 |
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351 | if (original.offspringPopulation != null) {
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352 | offspringPopulation = original.offspringPopulation.Select(cloner.Clone).ToArray();
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353 | }
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354 |
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355 | //if (original.jointPopulation != null) {
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356 | // jointPopulation = original.jointPopulation.Select(x => cloner.Clone(x)).ToArray();
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357 | //}
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358 |
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359 | if (original.executionContext != null) {
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360 | executionContext = cloner.Clone(original.executionContext);
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361 | }
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362 |
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363 | if (original.globalScope != null) {
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364 | globalScope = cloner.Clone(original.globalScope);
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365 | }
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366 | }
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367 |
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368 |
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369 |
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370 | [StorableConstructor]
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371 | protected DynamicALPSAlgorithmBase(StorableConstructorFlag deserializing) : base(deserializing) { }
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372 | #endregion
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373 |
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374 | private void InitializePopulation(ExecutionContext executionContext, CancellationToken cancellationToken, IRandom random, bool[] maximization) {
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375 | // creator: how to create the initilized population. "UniformRandom" is used here.
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376 | // TODO: LHS, latin hypercube sampling? Exisit???
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377 | var creator = Problem.SolutionCreator;
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378 | var evaluator = Problem.Evaluator;
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379 |
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380 | // dimensions: objective space
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381 | var dimensions = maximization.Length;
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382 | var populationSize = PopulationSize.Value;
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383 | population = new IDynamicALPSSolution[populationSize];
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384 |
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385 | var parentScope = executionContext.Scope;
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386 | // first, create all individuals
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387 | for (int i = 0; i < populationSize; ++i) {
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388 | var childScope = new Scope(i.ToString()) { Parent = parentScope };
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389 | ExecuteOperation(executionContext, cancellationToken, executionContext.CreateChildOperation(creator, childScope));
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390 | parentScope.SubScopes.Add(childScope);
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391 | }
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392 |
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393 | for (int i = 0; i < populationSize; ++i) {
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394 | var childScope = parentScope.SubScopes[i];
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395 | ExecuteOperation(executionContext, cancellationToken, executionContext.CreateChildOperation(evaluator, childScope));
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396 |
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397 | var qualities = (DoubleArray)childScope.Variables["Qualities"].Value;
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398 |
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399 | // solution: a method, contains a decision vector and objecitve values
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400 | // solution.Qualities: objective values, fitness values
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401 | // solution.Individual: decision vector
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402 | var solution = new DynamicALPSSolution(childScope, dimensions, 0);
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403 | for (int j = 0; j < dimensions; ++j) {
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404 | // TODO: convert maximization problems into minimization problems.
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405 | solution.Qualities[j] = maximization[j] ? 1 - qualities[j] : qualities[j];
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406 | }
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407 |
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408 | // population is a collection of solution.
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409 | population[i] = solution;
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410 |
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411 | // KF, DyanmicALPS
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412 | population[i].HypervolumeContribution[0] = -0;
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413 | population[i].NondominanceRanking[0] = -0;
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414 | population[i].Age = 1;
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415 | population[i].IndividualPc = CrossoverProbability.Value;
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416 | population[i].IndividualPc = MutationProbability.Value;
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417 | }
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418 | }
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419 |
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420 | protected void InitializeAlgorithm(CancellationToken cancellationToken) { // Type of random operator, "FastRandom" in this script.
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421 | // RandomParameter <-- Parameters in "HeuristicLab.Core.ParameterizedNameItem",
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422 | var rand = RandomParameter.Value;
|
---|
423 |
|
---|
424 | // Initialize random seed
|
---|
425 | // If random seed exist, get it; otherwise,
|
---|
426 | if (SetSeedRandomly) Seed = RandomSeedGenerator.GetSeed();
|
---|
427 |
|
---|
428 | // Call
|
---|
429 | rand.Reset(Seed);
|
---|
430 |
|
---|
431 | bool[] maximization = ((BoolArray)Problem.MaximizationParameter.ActualValue).CloneAsArray();
|
---|
432 |
|
---|
433 | // dimensions: the dimension in an objective space
|
---|
434 | var dimensions = maximization.Length;
|
---|
435 |
|
---|
436 |
|
---|
437 | var populationSize = PopulationSize.Value;
|
---|
438 |
|
---|
439 | InitializePopulation(executionContext, cancellationToken, rand, maximization);
|
---|
440 |
|
---|
441 | IdealPoint = new double[dimensions];
|
---|
442 | IdealPoint.UpdateIdeal(population);
|
---|
443 |
|
---|
444 | NadirPoint = Enumerable.Repeat(double.MinValue, dimensions).ToArray();
|
---|
445 | //NadirPoint = new double[dimensions];
|
---|
446 | NadirPoint.UpdateNadir(population);
|
---|
447 |
|
---|
448 |
|
---|
449 | evaluatedSolutions = populationSize;
|
---|
450 | }
|
---|
451 |
|
---|
452 | protected override void Initialize(CancellationToken cancellationToken) {
|
---|
453 | globalScope = new Scope("Global Scope");
|
---|
454 | executionContext = new ExecutionContext(null, this, globalScope);
|
---|
455 |
|
---|
456 | // set the execution context for parameters to allow lookup
|
---|
457 | foreach (var parameter in Problem.Parameters.OfType<IValueParameter>()) {
|
---|
458 | // we need all of these in order for the wiring of the operators to work
|
---|
459 | globalScope.Variables.Add(new Variable(parameter.Name, parameter.Value));
|
---|
460 | }
|
---|
461 | globalScope.Variables.Add(new Variable("Results", Results)); // make results available as a parameter for analyzers etc.
|
---|
462 |
|
---|
463 | base.Initialize(cancellationToken);
|
---|
464 | }
|
---|
465 |
|
---|
466 | public override bool SupportsPause => true;
|
---|
467 |
|
---|
468 |
|
---|
469 |
|
---|
470 |
|
---|
471 | // Mate Selection.
|
---|
472 | // Randomly select a specific number of individuals for later operators.
|
---|
473 | // Inputs:
|
---|
474 | // 1. random: Random number generate method
|
---|
475 | // 2. numberOfSolutionToSelect: The number of selection
|
---|
476 | // Outputs:
|
---|
477 | // 1. listOfSolutions: The selection individuals
|
---|
478 | protected List<int> MatingSelection(IRandom random, int numberOfSolutionsToSelect) {
|
---|
479 | int populationSize = PopulationSize.Value;
|
---|
480 |
|
---|
481 | var listOfSolutions = new List<int>(numberOfSolutionsToSelect);
|
---|
482 |
|
---|
483 | while (listOfSolutions.Count < numberOfSolutionsToSelect) {
|
---|
484 | var selectedSolution = random.Next(populationSize);
|
---|
485 |
|
---|
486 | bool flag = true;
|
---|
487 | foreach (int individualId in listOfSolutions) {
|
---|
488 | if (individualId == selectedSolution) {
|
---|
489 | flag = false;
|
---|
490 | break;
|
---|
491 | }
|
---|
492 | }
|
---|
493 |
|
---|
494 | if (flag) {
|
---|
495 | listOfSolutions.Add(selectedSolution);
|
---|
496 | }
|
---|
497 | }
|
---|
498 | return listOfSolutions;
|
---|
499 | }
|
---|
500 |
|
---|
501 | protected void ApplyCrossover(int lambda) {
|
---|
502 | }
|
---|
503 |
|
---|
504 | protected void ApplyMutation(int lambda) {
|
---|
505 | }
|
---|
506 |
|
---|
507 |
|
---|
508 | protected void ApplyEvaluation(int lambda) {
|
---|
509 | }
|
---|
510 |
|
---|
511 | protected void ApplyMateSelection(int rho) {
|
---|
512 | }
|
---|
513 |
|
---|
514 |
|
---|
515 | // Select/Discard the individual(s) according to HVC
|
---|
516 | protected void SmetricSelection(int lambda, int nLayerALPS) {
|
---|
517 | var wholePopulation = layerJointPopulation[nLayerALPS];
|
---|
518 | var qualities = wholePopulation.Select(x => x.Qualities).ToArray();
|
---|
519 |
|
---|
520 | var maximization = Enumerable.Repeat(false, IdealPoint.Length).ToArray(); // Minimization or maximization ????
|
---|
521 | var pf2 = DominationCalculator<IDynamicALPSSolution>.CalculateAllParetoFronts(wholePopulation, qualities, maximization, out int[] ranking);
|
---|
522 |
|
---|
523 | int numberOfLayer; // number of layers in PF
|
---|
524 | int numberOfLastLayer; // number of discarded points in PF (the number of points in the last layer)
|
---|
525 |
|
---|
526 | pf2.RemoveAt(pf2.Count() - 1);
|
---|
527 | numberOfLayer = pf2.Count();
|
---|
528 | numberOfLastLayer = pf2[numberOfLayer - 1].Count();
|
---|
529 | double[] hvc = new double[numberOfLastLayer];
|
---|
530 | int discardIndex;
|
---|
531 | if (numberOfLastLayer > lambda) {
|
---|
532 | double tempHV;
|
---|
533 | double smetric;
|
---|
534 | var lastLayer = pf2.Last();
|
---|
535 |
|
---|
536 | // TODO: This can be use for dynamic reference point strategy later. Kaifeng , 02/2020
|
---|
537 | // smetric = Hypervolume.Calculate(lastLayer.Select(x => x.Item2), Enumerable.Repeat(11d, NadirPoint.Length).ToArray(), maximization);
|
---|
538 | var reference = ReferencePoint.ToArray();
|
---|
539 | var nondominated = NonDominatedSelect.GetDominatingVectors(lastLayer.Select(x => x.Item2), reference, maximization, false);
|
---|
540 | smetric = nondominated.Any() ? Hypervolume.Calculate(nondominated, reference, maximization) : int.MinValue;
|
---|
541 |
|
---|
542 | for (int ii = 0; ii < lastLayer.Count; ++ii) {
|
---|
543 | try { // TODO: This can be use for dynamic reference point strategy later. Kaifeng , 02/2020
|
---|
544 | // tempHV = Hypervolume.Calculate(indices.Where(idx => idx != ii).Select(idx => lastLayer[idx].Item2), Enumerable.Repeat(11d, NadirPoint.Length).ToArray(), maximization);
|
---|
545 | tempHV = Hypervolume.Calculate(Enumerable.Range(0, lastLayer.Count).Where(idx => idx != ii).Select(idx => lastLayer[idx].Item2), reference, maximization);
|
---|
546 | }
|
---|
547 | catch {
|
---|
548 | tempHV = int.MinValue;
|
---|
549 | }
|
---|
550 | hvc[ii] = smetric - tempHV;
|
---|
551 | tempHV = 0;
|
---|
552 | }
|
---|
553 | discardIndex = Array.IndexOf(hvc, hvc.Min());
|
---|
554 | pf2[numberOfLayer - 1].RemoveAt(discardIndex);
|
---|
555 | }
|
---|
556 | else {
|
---|
557 | // TODO: This should be updated when $lambda > 1$
|
---|
558 | pf2.RemoveAt(pf2.Count() - 1);
|
---|
559 | numberOfLayer = numberOfLayer - 1;
|
---|
560 | }
|
---|
561 | layerPopulation[nLayerALPS] = pf2.SelectMany(x => x.Select(y => y.Item1)).ToArray();
|
---|
562 | }
|
---|
563 |
|
---|
564 | protected int SMSEMOA(int populationSize, int lambda, int counterLayerALPS) {
|
---|
565 | var innerToken = new CancellationToken();
|
---|
566 | bool[] maximization = ((BoolArray)Problem.MaximizationParameter.ActualValue).CloneAsArray();
|
---|
567 | var maximumEvaluatedSolutions = MaximumEvaluatedSolutions.Value;
|
---|
568 | var crossover = Crossover;
|
---|
569 | var crossoverProbability = CrossoverProbability.Value;
|
---|
570 | var mutator = Mutator;
|
---|
571 | var mutationProbability = MutationProbability.Value;
|
---|
572 | var evaluator = Problem.Evaluator;
|
---|
573 | var analyzer = Analyzer;
|
---|
574 | var rand = RandomParameter.Value;
|
---|
575 |
|
---|
576 |
|
---|
577 | int indexOffspring = 0;
|
---|
578 | var mates = MatingSelection(rand, 2); // select parents
|
---|
579 | //var s1 = (IScope)population[mates[0]].Individual.Clone();
|
---|
580 | //var s2 = (IScope)population[mates[1]].Individual.Clone();
|
---|
581 | //var ages = population.Select(x => x.Age).ToArray();
|
---|
582 |
|
---|
583 | var s1 = (IScope)layerPopulation[counterLayerALPS][mates[0]].Individual.Clone();
|
---|
584 | var s2 = (IScope)layerPopulation[counterLayerALPS][mates[1]].Individual.Clone();
|
---|
585 | var ages = layerPopulation[counterLayerALPS].Select(x => x.Age).ToArray();
|
---|
586 |
|
---|
587 | var s1_age = ages[mates[0]];
|
---|
588 | var s2_age = ages[mates[1]];
|
---|
589 | int offSpringAge = 0;
|
---|
590 | s1.Parent = s2.Parent = globalScope;
|
---|
591 | IScope childScope = null;
|
---|
592 |
|
---|
593 | // crossover
|
---|
594 | if (rand.NextDouble() < crossoverProbability) {
|
---|
595 | childScope = new Scope($"{mates[0]}+{mates[1]}") { Parent = executionContext.Scope };
|
---|
596 | childScope.SubScopes.Add(s1);
|
---|
597 | childScope.SubScopes.Add(s2);
|
---|
598 | var opCrossover = executionContext.CreateChildOperation(crossover, childScope);
|
---|
599 | ExecuteOperation(executionContext, innerToken, opCrossover);
|
---|
600 | offSpringAge = Math.Max(s1_age, s2_age) + 1;
|
---|
601 | childScope.SubScopes.Clear(); // <<-- VERY IMPORTANT!
|
---|
602 | }
|
---|
603 | else { // MUTATION POLISHI
|
---|
604 | if (childScope == null) {
|
---|
605 | offSpringAge = ages[mates[0]] + 1;
|
---|
606 | }
|
---|
607 | else {
|
---|
608 | }
|
---|
609 | childScope = childScope ?? s1;
|
---|
610 | var opMutation = executionContext.CreateChildOperation(mutator, childScope);
|
---|
611 | ExecuteOperation(executionContext, innerToken, opMutation);
|
---|
612 | }
|
---|
613 | if (childScope != null) { // Evaluate the childScope
|
---|
614 | var opEvaluation = executionContext.CreateChildOperation(evaluator, childScope);
|
---|
615 | ExecuteOperation(executionContext, innerToken, opEvaluation);
|
---|
616 | // childScope
|
---|
617 | var qualities = (DoubleArray)childScope.Variables["Qualities"].Value;
|
---|
618 | var childSolution = new DynamicALPSSolution(childScope, maximization.Length, 0);
|
---|
619 | // set child qualities
|
---|
620 | for (int j = 0; j < maximization.Length; ++j) {
|
---|
621 | childSolution.Qualities[j] = maximization[j] ? 1 - qualities[j] : qualities[j];
|
---|
622 | }
|
---|
623 | IdealPoint.UpdateIdeal(childSolution.Qualities);
|
---|
624 | NadirPoint.UpdateNadir(childSolution.Qualities);
|
---|
625 | // TODO, KF -- For later usage when $lambda > 1$
|
---|
626 | childSolution.HypervolumeContribution = null;
|
---|
627 | childSolution.NondominanceRanking = null;
|
---|
628 | childSolution.Age = offSpringAge;
|
---|
629 | layerOffspringPopulation[counterLayerALPS][indexOffspring] = childSolution;
|
---|
630 | ++evaluatedSolutions;
|
---|
631 | indexOffspring += 1;
|
---|
632 | }
|
---|
633 | else {
|
---|
634 | // no crossover or mutation were applied, a child was not produced, do nothing
|
---|
635 | }
|
---|
636 |
|
---|
637 |
|
---|
638 | layerJointPopulation[counterLayerALPS] = new IDynamicALPSSolution[populationSize + lambda];
|
---|
639 | layerPopulation[counterLayerALPS].CopyTo(layerJointPopulation[counterLayerALPS], 0);
|
---|
640 | layerOffspringPopulation[counterLayerALPS].CopyTo(layerJointPopulation[counterLayerALPS], populationSize);
|
---|
641 |
|
---|
642 | SmetricSelection(lambda, counterLayerALPS); // SMS-EMOA
|
---|
643 | return evaluatedSolutions;
|
---|
644 | }
|
---|
645 |
|
---|
646 |
|
---|
647 |
|
---|
648 |
|
---|
649 |
|
---|
650 |
|
---|
651 |
|
---|
652 | // Update the Pareto-front approximation set and scatter the solutions in PF approximation set.
|
---|
653 | protected void UpdateParetoFronts() {
|
---|
654 | //var qualities = population.Select(x => Enumerable.Range(0, NadirPoint.Length).Select(i => x.Qualities[i] / NadirPoint[i]).ToArray()).ToArray();
|
---|
655 | var qualities = population.Select(x => x.Qualities).ToArray();
|
---|
656 | var maximization = Enumerable.Repeat(false, IdealPoint.Length).ToArray(); // DynamicALPS minimizes everything internally
|
---|
657 | var pf = DominationCalculator<IDynamicALPSSolution>.CalculateBestParetoFront(population, qualities, maximization);
|
---|
658 |
|
---|
659 | var pf2 = DominationCalculator<IDynamicALPSSolution>.CalculateAllParetoFronts(population, qualities, maximization, out int[] ranking);
|
---|
660 | var n = (int)EnumerableExtensions.BinomialCoefficient(IdealPoint.Length, 2);
|
---|
661 |
|
---|
662 |
|
---|
663 | // Struture hypervolume
|
---|
664 | // [0,0]: Value of HV
|
---|
665 | // [0,1]: PF size, $|PF|$
|
---|
666 | var hypervolumes = new DoubleMatrix(n == 1 ? 1 : n + 1, 2) { ColumnNames = new[] { "PF hypervolume", "PF size" } };
|
---|
667 |
|
---|
668 |
|
---|
669 | // HV calculation
|
---|
670 | // pf.Select(x => x.Item2): the "Item2" in var "pd"
|
---|
671 | // Enumerable.Repeat(1d, NadirPoint.Length).ToArray(): reference point
|
---|
672 | // maximization: type of optimization problem:
|
---|
673 | // True: maximization problem
|
---|
674 | // False: minimization problem
|
---|
675 | var reference = ReferencePoint.ToArray();
|
---|
676 | var nondominated = NonDominatedSelect.GetDominatingVectors(pf.Select(x => x.Item2), reference, maximization, false);
|
---|
677 | hypervolumes[0, 0] = nondominated.Any() ? Hypervolume.Calculate(nondominated, reference, maximization) : int.MinValue;
|
---|
678 |
|
---|
679 | //hypervolumes[0, 0] = Hypervolume.Calculate(pf.Select(x => x.Item2), reference, maximization);
|
---|
680 | hypervolumes[0, 1] = pf.Count;
|
---|
681 | Console.WriteLine("Current HV is", hypervolumes[0, 0]);
|
---|
682 |
|
---|
683 | var elementNames = new List<string>() { "Pareto Front" };
|
---|
684 |
|
---|
685 | ResultCollection results;
|
---|
686 | if (Results.ContainsKey("Hypervolume Analysis")) {
|
---|
687 | results = (ResultCollection)Results["Hypervolume Analysis"].Value;
|
---|
688 | }
|
---|
689 | else {
|
---|
690 | results = new ResultCollection();
|
---|
691 | Results.AddOrUpdateResult("Hypervolume Analysis", results);
|
---|
692 | }
|
---|
693 |
|
---|
694 | ScatterPlot sp;
|
---|
695 | if (IdealPoint.Length == 2) {
|
---|
696 | var points = pf.Select(x => new Point2D<double>(x.Item2[0], x.Item2[1]));
|
---|
697 | var r = OnlinePearsonsRCalculator.Calculate(points.Select(x => x.X), points.Select(x => x.Y), out OnlineCalculatorError error);
|
---|
698 | if (error != OnlineCalculatorError.None) { r = double.NaN; }
|
---|
699 | var resultName = "Pareto Front Analysis ";
|
---|
700 | if (!results.ContainsKey(resultName)) {
|
---|
701 | sp = new ScatterPlot() {
|
---|
702 | VisualProperties = {
|
---|
703 | XAxisMinimumAuto = false, XAxisMinimumFixedValue = 0d, XAxisMaximumAuto = false, XAxisMaximumFixedValue = 1d,
|
---|
704 | YAxisMinimumAuto = false, YAxisMinimumFixedValue = 0d, YAxisMaximumAuto = false, YAxisMaximumFixedValue = 1d
|
---|
705 | }
|
---|
706 | };
|
---|
707 | sp.Rows.Add(new ScatterPlotDataRow(resultName, "", points) { VisualProperties = { PointSize = 8 } });
|
---|
708 | results.AddOrUpdateResult(resultName, sp);
|
---|
709 | }
|
---|
710 | else {
|
---|
711 | sp = (ScatterPlot)results[resultName].Value;
|
---|
712 | sp.Rows[resultName].Points.Replace(points);
|
---|
713 | }
|
---|
714 | sp.Name = $"Dimensions [0, 1], correlation: {r.ToString("N2")}";
|
---|
715 | }
|
---|
716 | else if (IdealPoint.Length > 2) {
|
---|
717 | var indices = Enumerable.Range(0, IdealPoint.Length).ToArray();
|
---|
718 | var visualProperties = new ScatterPlotDataRowVisualProperties { PointSize = 8, Color = Color.LightGray };
|
---|
719 | var combinations = indices.Combinations(2).ToArray();
|
---|
720 | var maximization2d = new[] { false, false };
|
---|
721 | var solutions2d = pf.Select(x => x.Item1).ToArray();
|
---|
722 | for (int i = 0; i < combinations.Length; ++i) {
|
---|
723 | var c = combinations[i].ToArray();
|
---|
724 |
|
---|
725 | // calculate the hypervolume in the 2d coordinate space
|
---|
726 | var reference2d = new[] { 1d, 1d };
|
---|
727 | var qualities2d = pf.Select(x => new[] { x.Item2[c[0]], x.Item2[c[1]] }).ToArray();
|
---|
728 | var pf2d = DominationCalculator<IDynamicALPSSolution>.CalculateBestParetoFront(solutions2d, qualities2d, maximization2d);
|
---|
729 |
|
---|
730 | hypervolumes[i + 1, 0] = pf2d.Count > 0 ? Hypervolume.Calculate(pf2d.Select(x => x.Item2), reference2d, maximization2d) : 0d;
|
---|
731 | hypervolumes[i + 1, 1] = pf2d.Count;
|
---|
732 |
|
---|
733 | var resultName = $"Pareto Front Analysis [{c[0]}, {c[1]}]";
|
---|
734 | elementNames.Add(resultName);
|
---|
735 |
|
---|
736 | var points = pf.Select(x => new Point2D<double>(x.Item2[c[0]], x.Item2[c[1]]));
|
---|
737 | var pf2dPoints = pf2d.Select(x => new Point2D<double>(x.Item2[0], x.Item2[1]));
|
---|
738 |
|
---|
739 | if (!results.ContainsKey(resultName)) {
|
---|
740 | sp = new ScatterPlot() {
|
---|
741 | VisualProperties = {
|
---|
742 | XAxisMinimumAuto = false, XAxisMinimumFixedValue = 0d, XAxisMaximumAuto = false, XAxisMaximumFixedValue = 1d,
|
---|
743 | YAxisMinimumAuto = false, YAxisMinimumFixedValue = 0d, YAxisMaximumAuto = false, YAxisMaximumFixedValue = 1d
|
---|
744 | }
|
---|
745 | };
|
---|
746 | sp.Rows.Add(new ScatterPlotDataRow("Pareto Front", "", points) { VisualProperties = visualProperties });
|
---|
747 | sp.Rows.Add(new ScatterPlotDataRow($"Pareto Front [{c[0]}, {c[1]}]", "", pf2dPoints) { VisualProperties = { PointSize = 10, Color = Color.OrangeRed } });
|
---|
748 | results.AddOrUpdateResult(resultName, sp);
|
---|
749 | }
|
---|
750 | else {
|
---|
751 | sp = (ScatterPlot)results[resultName].Value;
|
---|
752 | sp.Rows["Pareto Front"].Points.Replace(points);
|
---|
753 | sp.Rows[$"Pareto Front [{c[0]}, {c[1]}]"].Points.Replace(pf2dPoints);
|
---|
754 | }
|
---|
755 | var r = OnlinePearsonsRCalculator.Calculate(points.Select(x => x.X), points.Select(x => x.Y), out OnlineCalculatorError error);
|
---|
756 | var r2 = r * r;
|
---|
757 | sp.Name = $"Pareto Front [{c[0]}, {c[1]}], correlation: {r2.ToString("N2")}";
|
---|
758 | }
|
---|
759 | }
|
---|
760 | hypervolumes.RowNames = elementNames;
|
---|
761 | results.AddOrUpdateResult("Hypervolumes", hypervolumes);
|
---|
762 | }
|
---|
763 |
|
---|
764 | #region operator wiring and events
|
---|
765 | protected void ExecuteOperation(ExecutionContext executionContext, CancellationToken cancellationToken, IOperation operation) {
|
---|
766 | Stack<IOperation> executionStack = new Stack<IOperation>();
|
---|
767 | executionStack.Push(operation);
|
---|
768 | while (executionStack.Count > 0) {
|
---|
769 | cancellationToken.ThrowIfCancellationRequested();
|
---|
770 | IOperation next = executionStack.Pop();
|
---|
771 | if (next is OperationCollection) {
|
---|
772 | OperationCollection coll = (OperationCollection)next;
|
---|
773 | for (int i = coll.Count - 1; i >= 0; i--)
|
---|
774 | if (coll[i] != null) executionStack.Push(coll[i]);
|
---|
775 | }
|
---|
776 | else if (next is IAtomicOperation) {
|
---|
777 | IAtomicOperation op = (IAtomicOperation)next;
|
---|
778 | next = op.Operator.Execute((IExecutionContext)op, cancellationToken);
|
---|
779 | if (next != null) executionStack.Push(next);
|
---|
780 | }
|
---|
781 | }
|
---|
782 | }
|
---|
783 |
|
---|
784 | private void UpdateAnalyzers() {
|
---|
785 | Analyzer.Operators.Clear();
|
---|
786 | if (Problem != null) {
|
---|
787 | foreach (IAnalyzer analyzer in Problem.Operators.OfType<IAnalyzer>()) {
|
---|
788 | foreach (IScopeTreeLookupParameter param in analyzer.Parameters.OfType<IScopeTreeLookupParameter>())
|
---|
789 | param.Depth = 1;
|
---|
790 | Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
|
---|
791 | }
|
---|
792 | }
|
---|
793 | }
|
---|
794 |
|
---|
795 | private void UpdateCrossovers() {
|
---|
796 | ICrossover oldCrossover = CrossoverParameter.Value;
|
---|
797 | CrossoverParameter.ValidValues.Clear();
|
---|
798 | ICrossover defaultCrossover = Problem.Operators.OfType<ICrossover>().FirstOrDefault();
|
---|
799 |
|
---|
800 | foreach (ICrossover crossover in Problem.Operators.OfType<ICrossover>().OrderBy(x => x.Name))
|
---|
801 | CrossoverParameter.ValidValues.Add(crossover);
|
---|
802 |
|
---|
803 | if (oldCrossover != null) {
|
---|
804 | ICrossover crossover = CrossoverParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldCrossover.GetType());
|
---|
805 | if (crossover != null) CrossoverParameter.Value = crossover;
|
---|
806 | else oldCrossover = null;
|
---|
807 | }
|
---|
808 | if (oldCrossover == null && defaultCrossover != null)
|
---|
809 | CrossoverParameter.Value = defaultCrossover;
|
---|
810 | }
|
---|
811 |
|
---|
812 | private void UpdateMutators() {
|
---|
813 | IManipulator oldMutator = MutatorParameter.Value;
|
---|
814 | MutatorParameter.ValidValues.Clear();
|
---|
815 | IManipulator defaultMutator = Problem.Operators.OfType<IManipulator>().FirstOrDefault();
|
---|
816 |
|
---|
817 | foreach (IManipulator mutator in Problem.Operators.OfType<IManipulator>().OrderBy(x => x.Name))
|
---|
818 | MutatorParameter.ValidValues.Add(mutator);
|
---|
819 |
|
---|
820 | if (oldMutator != null) {
|
---|
821 | IManipulator mutator = MutatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldMutator.GetType());
|
---|
822 | if (mutator != null) MutatorParameter.Value = mutator;
|
---|
823 | else oldMutator = null;
|
---|
824 | }
|
---|
825 |
|
---|
826 | if (oldMutator == null && defaultMutator != null)
|
---|
827 | MutatorParameter.Value = defaultMutator;
|
---|
828 | }
|
---|
829 |
|
---|
830 | protected override void OnProblemChanged() {
|
---|
831 | UpdateCrossovers();
|
---|
832 | UpdateMutators();
|
---|
833 | UpdateAnalyzers();
|
---|
834 | base.OnProblemChanged();
|
---|
835 | }
|
---|
836 |
|
---|
837 | protected override void OnExecutionStateChanged() {
|
---|
838 | previousExecutionState = executionState;
|
---|
839 | executionState = ExecutionState;
|
---|
840 | base.OnExecutionStateChanged();
|
---|
841 | }
|
---|
842 |
|
---|
843 | public void ClearState() {
|
---|
844 | solutions = null;
|
---|
845 | population = null;
|
---|
846 | offspringPopulation = null;
|
---|
847 | //jointPopulation = null;
|
---|
848 | lambda_moead = null;
|
---|
849 | neighbourhood = null;
|
---|
850 | if (executionContext != null && executionContext.Scope != null) {
|
---|
851 | executionContext.Scope.SubScopes.Clear();
|
---|
852 | }
|
---|
853 | }
|
---|
854 |
|
---|
855 | protected override void OnStopped() {
|
---|
856 | ClearState();
|
---|
857 | base.OnStopped();
|
---|
858 | }
|
---|
859 | #endregion
|
---|
860 | }
|
---|
861 | }
|
---|