1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Collections.Generic;
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24 | using System.Linq;
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25 | using System.Runtime.CompilerServices;
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26 | using System.Threading;
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27 | using HeuristicLab.Algorithms.DataAnalysis;
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28 | using HeuristicLab.Algorithms.MemPR.Interfaces;
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29 | using HeuristicLab.Common;
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30 | using HeuristicLab.Core;
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31 | using HeuristicLab.Data;
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32 | using HeuristicLab.Optimization;
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33 | using HeuristicLab.Parameters;
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34 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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35 | using HeuristicLab.Problems.DataAnalysis;
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36 | using HeuristicLab.Random;
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37 | using ExecutionContext = HeuristicLab.Core.ExecutionContext;
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38 |
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39 | namespace HeuristicLab.Algorithms.MemPR {
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40 | [Item("MemPRContext", "Abstract base class for MemPR contexts.")]
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41 | [StorableClass]
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42 | public abstract class MemPRPopulationContext<TProblem, TSolution, TPopulationContext, TSolutionContext> : ParameterizedNamedItem,
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43 | IPopulationBasedHeuristicAlgorithmContext<TProblem, TSolution>, ISolutionModelContext<TSolution>
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44 | where TProblem : class, IItem, ISingleObjectiveProblemDefinition
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45 | where TSolution : class, IItem
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46 | where TPopulationContext : MemPRPopulationContext<TProblem, TSolution, TPopulationContext, TSolutionContext>
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47 | where TSolutionContext : MemPRSolutionContext<TProblem, TSolution, TPopulationContext, TSolutionContext> {
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48 |
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49 | private IExecutionContext parent;
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50 | public IExecutionContext Parent {
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51 | get { return parent; }
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52 | set { parent = value; }
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53 | }
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54 |
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55 | [Storable]
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56 | private IScope scope;
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57 | public IScope Scope {
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58 | get { return scope; }
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59 | private set { scope = value; }
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60 | }
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61 |
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62 | IKeyedItemCollection<string, IParameter> IExecutionContext.Parameters {
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63 | get { return Parameters; }
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64 | }
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65 |
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66 | [Storable]
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67 | private IValueParameter<TProblem> problem;
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68 | public TProblem Problem {
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69 | get { return problem.Value; }
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70 | set { problem.Value = value; }
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71 | }
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72 |
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73 | [Storable]
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74 | private IValueParameter<BoolValue> initialized;
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75 | public bool Initialized {
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76 | get { return initialized.Value.Value; }
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77 | set { initialized.Value.Value = value; }
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78 | }
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79 |
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80 | [Storable]
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81 | private IValueParameter<IntValue> iterations;
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82 | public int Iterations {
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83 | get { return iterations.Value.Value; }
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84 | set { iterations.Value.Value = value; }
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85 | }
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86 |
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87 | [Storable]
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88 | private IValueParameter<IntValue> evaluatedSolutions;
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89 | public int EvaluatedSolutions {
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90 | get { return evaluatedSolutions.Value.Value; }
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91 | set { evaluatedSolutions.Value.Value = value; }
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92 | }
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93 |
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94 | [Storable]
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95 | private IValueParameter<DoubleValue> bestQuality;
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96 | public double BestQuality {
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97 | get { return bestQuality.Value.Value; }
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98 | set { bestQuality.Value.Value = value; }
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99 | }
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100 |
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101 | [Storable]
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102 | private IValueParameter<TSolution> bestSolution;
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103 | public TSolution BestSolution {
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104 | get { return bestSolution.Value; }
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105 | set { bestSolution.Value = value; }
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106 | }
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107 |
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108 | [Storable]
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109 | private IValueParameter<IntValue> localSearchEvaluations;
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110 | public int LocalSearchEvaluations {
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111 | get { return localSearchEvaluations.Value.Value; }
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112 | set { localSearchEvaluations.Value.Value = value; }
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113 | }
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114 |
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115 | [Storable]
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116 | private IValueParameter<IntValue> byBreeding;
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117 | public int ByBreeding {
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118 | get { return byBreeding.Value.Value; }
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119 | set { byBreeding.Value.Value = value; }
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120 | }
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121 |
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122 | [Storable]
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123 | private IValueParameter<IntValue> byRelinking;
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124 | public int ByRelinking {
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125 | get { return byRelinking.Value.Value; }
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126 | set { byRelinking.Value.Value = value; }
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127 | }
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128 |
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129 | [Storable]
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130 | private IValueParameter<IntValue> byDelinking;
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131 | public int ByDelinking {
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132 | get { return byDelinking.Value.Value; }
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133 | set { byDelinking.Value.Value = value; }
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134 | }
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135 |
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136 | [Storable]
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137 | private IValueParameter<IntValue> bySampling;
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138 | public int BySampling {
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139 | get { return bySampling.Value.Value; }
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140 | set { bySampling.Value.Value = value; }
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141 | }
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142 |
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143 | [Storable]
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144 | private IValueParameter<IntValue> byHillclimbing;
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145 | public int ByHillclimbing {
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146 | get { return byHillclimbing.Value.Value; }
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147 | set { byHillclimbing.Value.Value = value; }
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148 | }
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149 |
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150 | [Storable]
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151 | private IValueParameter<IntValue> byAdaptivewalking;
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152 | public int ByAdaptivewalking {
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153 | get { return byAdaptivewalking.Value.Value; }
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154 | set { byAdaptivewalking.Value.Value = value; }
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155 | }
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156 |
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157 | [Storable]
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158 | private IValueParameter<IRandom> random;
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159 | public IRandom Random {
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160 | get { return random.Value; }
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161 | set { random.Value = value; }
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162 | }
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163 |
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164 | public IEnumerable<ISingleObjectiveSolutionScope<TSolution>> Population {
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165 | get { return scope.SubScopes.OfType<ISingleObjectiveSolutionScope<TSolution>>(); }
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166 | }
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167 | public void AddToPopulation(ISingleObjectiveSolutionScope<TSolution> solScope) {
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168 | scope.SubScopes.Add(solScope);
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169 | }
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170 | public void ReplaceAtPopulation(int index, ISingleObjectiveSolutionScope<TSolution> solScope) {
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171 | scope.SubScopes[index] = solScope;
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172 | }
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173 | public ISingleObjectiveSolutionScope<TSolution> AtPopulation(int index) {
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174 | return scope.SubScopes[index] as ISingleObjectiveSolutionScope<TSolution>;
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175 | }
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176 | public void SortPopulation() {
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177 | scope.SubScopes.Replace(scope.SubScopes.OfType<ISingleObjectiveSolutionScope<TSolution>>().OrderBy(x => Problem.Maximization ? -x.Fitness : x.Fitness).ToList());
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178 | }
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179 | public int PopulationCount {
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180 | get { return scope.SubScopes.Count; }
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181 | }
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182 |
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183 | [Storable]
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184 | private IConfidenceRegressionModel breedingPerformanceModel;
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185 | public IConfidenceRegressionModel BreedingPerformanceModel {
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186 | get { return breedingPerformanceModel; }
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187 | }
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188 | [Storable]
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189 | private List<Tuple<double, double, double>> breedingStat;
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190 | public List<Tuple<double, double, double>> BreedingStat {
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191 | get { return breedingStat; }
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192 | }
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193 | [Storable]
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194 | private IConfidenceRegressionModel relinkingPerformanceModel;
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195 | public IConfidenceRegressionModel RelinkingPerformanceModel {
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196 | get { return relinkingPerformanceModel; }
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197 | }
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198 | [Storable]
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199 | private List<Tuple<double, double, double>> relinkingStat;
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200 | public List<Tuple<double, double, double>> RelinkingStat {
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201 | get { return relinkingStat; }
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202 | }
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203 | [Storable]
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204 | private IConfidenceRegressionModel delinkingPerformanceModel;
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205 | public IConfidenceRegressionModel DelinkingPerformanceModel {
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206 | get { return delinkingPerformanceModel; }
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207 | }
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208 | [Storable]
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209 | private List<Tuple<double, double, double>> delinkingStat;
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210 | public List<Tuple<double, double, double>> DelinkingStat {
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211 | get { return delinkingStat; }
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212 | }
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213 | [Storable]
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214 | private IConfidenceRegressionModel samplingPerformanceModel;
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215 | public IConfidenceRegressionModel SamplingPerformanceModel {
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216 | get { return samplingPerformanceModel; }
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217 | }
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218 | [Storable]
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219 | private List<Tuple<double, double>> samplingStat;
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220 | public List<Tuple<double, double>> SamplingStat {
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221 | get { return samplingStat; }
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222 | }
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223 | [Storable]
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224 | private IConfidenceRegressionModel hillclimbingPerformanceModel;
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225 | public IConfidenceRegressionModel HillclimbingPerformanceModel {
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226 | get { return hillclimbingPerformanceModel; }
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227 | }
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228 | [Storable]
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229 | private List<Tuple<double, double>> hillclimbingStat;
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230 | public List<Tuple<double, double>> HillclimbingStat {
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231 | get { return hillclimbingStat; }
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232 | }
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233 | [Storable]
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234 | private IConfidenceRegressionModel adaptiveWalkPerformanceModel;
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235 | public IConfidenceRegressionModel AdaptiveWalkPerformanceModel {
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236 | get { return adaptiveWalkPerformanceModel; }
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237 | }
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238 | [Storable]
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239 | private List<Tuple<double, double>> adaptivewalkingStat;
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240 | public List<Tuple<double, double>> AdaptivewalkingStat {
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241 | get { return adaptivewalkingStat; }
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242 | }
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243 |
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244 | [Storable]
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245 | public ISolutionModel<TSolution> Model { get; set; }
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246 |
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247 | [StorableConstructor]
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248 | protected MemPRPopulationContext(bool deserializing) : base(deserializing) { }
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249 | protected MemPRPopulationContext(MemPRPopulationContext<TProblem, TSolution, TPopulationContext, TSolutionContext> original, Cloner cloner)
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250 | : base(original, cloner) {
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251 | scope = cloner.Clone(original.scope);
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252 | problem = cloner.Clone(original.problem);
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253 | initialized = cloner.Clone(original.initialized);
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254 | iterations = cloner.Clone(original.iterations);
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255 | evaluatedSolutions = cloner.Clone(original.evaluatedSolutions);
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256 | bestQuality = cloner.Clone(original.bestQuality);
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257 | bestSolution = cloner.Clone(original.bestSolution);
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258 | localSearchEvaluations = cloner.Clone(original.localSearchEvaluations);
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259 | byBreeding = cloner.Clone(original.byBreeding);
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260 | byRelinking = cloner.Clone(original.byRelinking);
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261 | byDelinking = cloner.Clone(original.byDelinking);
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262 | bySampling = cloner.Clone(original.bySampling);
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263 | byHillclimbing = cloner.Clone(original.byHillclimbing);
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264 | byAdaptivewalking = cloner.Clone(original.byAdaptivewalking);
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265 | random = cloner.Clone(original.random);
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266 | breedingPerformanceModel = cloner.Clone(original.breedingPerformanceModel);
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267 | breedingStat = original.breedingStat.Select(x => Tuple.Create(x.Item1, x.Item2, x.Item3)).ToList();
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268 | relinkingPerformanceModel = cloner.Clone(original.relinkingPerformanceModel);
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269 | relinkingStat = original.relinkingStat.Select(x => Tuple.Create(x.Item1, x.Item2, x.Item3)).ToList();
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270 | delinkingPerformanceModel = cloner.Clone(original.delinkingPerformanceModel);
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271 | delinkingStat = original.delinkingStat.Select(x => Tuple.Create(x.Item1, x.Item2, x.Item3)).ToList();
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272 | samplingPerformanceModel = cloner.Clone(original.samplingPerformanceModel);
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273 | samplingStat = original.samplingStat.Select(x => Tuple.Create(x.Item1, x.Item2)).ToList();
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274 | hillclimbingPerformanceModel = cloner.Clone(original.hillclimbingPerformanceModel);
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275 | hillclimbingStat = original.hillclimbingStat.Select(x => Tuple.Create(x.Item1, x.Item2)).ToList();
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276 | adaptiveWalkPerformanceModel = cloner.Clone(original.adaptiveWalkPerformanceModel);
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277 | adaptivewalkingStat = original.adaptivewalkingStat.Select(x => Tuple.Create(x.Item1, x.Item2)).ToList();
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278 |
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279 | Model = cloner.Clone(original.Model);
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280 | }
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281 | public MemPRPopulationContext() : this("MemPRContext") { }
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282 | public MemPRPopulationContext(string name) : base(name) {
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283 | scope = new Scope("Global");
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284 |
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285 | Parameters.Add(problem = new ValueParameter<TProblem>("Problem"));
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286 | Parameters.Add(initialized = new ValueParameter<BoolValue>("Initialized", new BoolValue(false)));
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287 | Parameters.Add(iterations = new ValueParameter<IntValue>("Iterations", new IntValue(0)));
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288 | Parameters.Add(evaluatedSolutions = new ValueParameter<IntValue>("EvaluatedSolutions", new IntValue(0)));
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289 | Parameters.Add(bestQuality = new ValueParameter<DoubleValue>("BestQuality", new DoubleValue(double.NaN)));
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290 | Parameters.Add(bestSolution = new ValueParameter<TSolution>("BestSolution"));
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291 | Parameters.Add(localSearchEvaluations = new ValueParameter<IntValue>("LocalSearchEvaluations", new IntValue(0)));
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292 | Parameters.Add(byBreeding = new ValueParameter<IntValue>("ByBreeding", new IntValue(0)));
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293 | Parameters.Add(byRelinking = new ValueParameter<IntValue>("ByRelinking", new IntValue(0)));
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294 | Parameters.Add(byDelinking = new ValueParameter<IntValue>("ByDelinking", new IntValue(0)));
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295 | Parameters.Add(bySampling = new ValueParameter<IntValue>("BySampling", new IntValue(0)));
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296 | Parameters.Add(byHillclimbing = new ValueParameter<IntValue>("ByHillclimbing", new IntValue(0)));
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297 | Parameters.Add(byAdaptivewalking = new ValueParameter<IntValue>("ByAdaptivewalking", new IntValue(0)));
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298 | Parameters.Add(random = new ValueParameter<IRandom>("Random", new MersenneTwister()));
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299 |
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300 | breedingStat = new List<Tuple<double, double, double>>();
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301 | relinkingStat = new List<Tuple<double, double, double>>();
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302 | delinkingStat = new List<Tuple<double, double, double>>();
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303 | samplingStat = new List<Tuple<double, double>>();
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304 | hillclimbingStat = new List<Tuple<double, double>>();
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305 | adaptivewalkingStat = new List<Tuple<double, double>>();
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306 | }
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307 |
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308 | public abstract TSolutionContext CreateSingleSolutionContext(ISingleObjectiveSolutionScope<TSolution> solution);
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309 |
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310 | public void IncrementEvaluatedSolutions(int byEvaluations) {
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311 | if (byEvaluations < 0) throw new ArgumentException("Can only increment and not decrement evaluated solutions.");
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312 | EvaluatedSolutions += byEvaluations;
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313 | }
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314 |
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315 | public void RelearnBreedingPerformanceModel() {
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316 | breedingPerformanceModel = RunRegression(PrepareRegression(BreedingStat), breedingPerformanceModel).Model;
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317 | }
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318 | public bool BreedingSuited(ISingleObjectiveSolutionScope<TSolution> p1, ISingleObjectiveSolutionScope<TSolution> p2) {
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319 | if (breedingPerformanceModel == null) return true;
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320 | double minI1 = double.MaxValue, minI2 = double.MaxValue, maxI1 = double.MinValue, maxI2 = double.MinValue;
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321 | foreach (var d in BreedingStat) {
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322 | if (d.Item1 < minI1) minI1 = d.Item1;
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323 | if (d.Item1 > maxI1) maxI1 = d.Item1;
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324 | if (d.Item2 < minI2) minI2 = d.Item2;
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325 | if (d.Item2 > maxI2) maxI2 = d.Item2;
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326 | }
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327 | if (IsBetter(p1, p2)) {
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328 | if (p1.Fitness < minI1 || p1.Fitness > maxI1 || p2.Fitness < minI2 || p2.Fitness > maxI2)
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329 | return true;
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330 | return Random.NextDouble() < ProbabilityAccept3dModel(p1.Fitness, p2.Fitness, breedingPerformanceModel);
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331 | }
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332 | if (p1.Fitness < minI2 || p1.Fitness > maxI2 || p2.Fitness < minI1 || p2.Fitness > maxI1)
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333 | return true;
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334 | return Random.NextDouble() < ProbabilityAccept3dModel(p2.Fitness, p1.Fitness, breedingPerformanceModel);
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335 | }
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336 |
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337 | public void RelearnRelinkingPerformanceModel() {
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338 | relinkingPerformanceModel = RunRegression(PrepareRegression(RelinkingStat), relinkingPerformanceModel).Model;
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339 | }
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340 | public bool RelinkSuited(ISingleObjectiveSolutionScope<TSolution> p1, ISingleObjectiveSolutionScope<TSolution> p2) {
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341 | if (relinkingPerformanceModel == null) return true;
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342 | double minI1 = double.MaxValue, minI2 = double.MaxValue, maxI1 = double.MinValue, maxI2 = double.MinValue;
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343 | foreach (var d in RelinkingStat) {
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344 | if (d.Item1 < minI1) minI1 = d.Item1;
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345 | if (d.Item1 > maxI1) maxI1 = d.Item1;
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346 | if (d.Item2 < minI2) minI2 = d.Item2;
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347 | if (d.Item2 > maxI2) maxI2 = d.Item2;
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348 | }
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349 | if (IsBetter(p1, p2)) {
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350 | if (p1.Fitness < minI1 || p1.Fitness > maxI1 || p2.Fitness < minI2 || p2.Fitness > maxI2)
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351 | return true;
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352 | return Random.NextDouble() < ProbabilityAccept3dModel(p1.Fitness, p2.Fitness, relinkingPerformanceModel);
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353 | }
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354 | if (p1.Fitness < minI2 || p1.Fitness > maxI2 || p2.Fitness < minI1 || p2.Fitness > maxI1)
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355 | return true;
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356 | return Random.NextDouble() < ProbabilityAccept3dModel(p2.Fitness, p1.Fitness, relinkingPerformanceModel);
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357 | }
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358 |
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359 | public void RelearnDelinkingPerformanceModel() {
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360 | delinkingPerformanceModel = RunRegression(PrepareRegression(DelinkingStat), delinkingPerformanceModel).Model;
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361 | }
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362 | public bool DelinkSuited(ISingleObjectiveSolutionScope<TSolution> p1, ISingleObjectiveSolutionScope<TSolution> p2) {
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363 | if (delinkingPerformanceModel == null) return true;
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364 | double minI1 = double.MaxValue, minI2 = double.MaxValue, maxI1 = double.MinValue, maxI2 = double.MinValue;
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365 | foreach (var d in DelinkingStat) {
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366 | if (d.Item1 < minI1) minI1 = d.Item1;
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367 | if (d.Item1 > maxI1) maxI1 = d.Item1;
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368 | if (d.Item2 < minI2) minI2 = d.Item2;
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369 | if (d.Item2 > maxI2) maxI2 = d.Item2;
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370 | }
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371 | if (IsBetter(p1, p2)) {
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372 | if (p1.Fitness < minI1 || p1.Fitness > maxI1 || p2.Fitness < minI2 || p2.Fitness > maxI2)
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373 | return true;
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374 | return Random.NextDouble() < ProbabilityAccept3dModel(p1.Fitness, p2.Fitness, delinkingPerformanceModel);
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375 | }
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376 | if (p1.Fitness < minI2 || p1.Fitness > maxI2 || p2.Fitness < minI1 || p2.Fitness > maxI1)
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377 | return true;
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378 | return Random.NextDouble() < ProbabilityAccept3dModel(p2.Fitness, p1.Fitness, delinkingPerformanceModel);
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379 | }
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380 |
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381 | public void RelearnSamplingPerformanceModel() {
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382 | samplingPerformanceModel = RunRegression(PrepareRegression(SamplingStat), samplingPerformanceModel).Model;
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383 | }
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384 | public bool SamplingSuited() {
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385 | if (samplingPerformanceModel == null) return true;
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386 | return Random.NextDouble() < ProbabilityAccept2dModel(Population.Average(x => x.Fitness), samplingPerformanceModel);
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387 | }
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---|
388 |
|
---|
389 | public void RelearnHillclimbingPerformanceModel() {
|
---|
390 | hillclimbingPerformanceModel = RunRegression(PrepareRegression(HillclimbingStat), hillclimbingPerformanceModel).Model;
|
---|
391 | }
|
---|
392 | public bool HillclimbingSuited(ISingleObjectiveSolutionScope<TSolution> scope) {
|
---|
393 | if (hillclimbingPerformanceModel == null) return true;
|
---|
394 | if (scope.Fitness < HillclimbingStat.Min(x => x.Item1) || scope.Fitness > HillclimbingStat.Max(x => x.Item1))
|
---|
395 | return true;
|
---|
396 | return Random.NextDouble() < ProbabilityAccept2dModel(scope.Fitness, hillclimbingPerformanceModel);
|
---|
397 | }
|
---|
398 | public bool HillclimbingSuited(double startingFitness) {
|
---|
399 | if (hillclimbingPerformanceModel == null) return true;
|
---|
400 | if (startingFitness < HillclimbingStat.Min(x => x.Item1) || startingFitness > HillclimbingStat.Max(x => x.Item1))
|
---|
401 | return true;
|
---|
402 | return Random.NextDouble() < ProbabilityAccept2dModel(startingFitness, hillclimbingPerformanceModel);
|
---|
403 | }
|
---|
404 |
|
---|
405 | public void RelearnAdaptiveWalkPerformanceModel() {
|
---|
406 | adaptiveWalkPerformanceModel = RunRegression(PrepareRegression(AdaptivewalkingStat), adaptiveWalkPerformanceModel).Model;
|
---|
407 | }
|
---|
408 | public bool AdaptivewalkingSuited(ISingleObjectiveSolutionScope<TSolution> scope) {
|
---|
409 | if (adaptiveWalkPerformanceModel == null) return true;
|
---|
410 | if (scope.Fitness < AdaptivewalkingStat.Min(x => x.Item1) || scope.Fitness > AdaptivewalkingStat.Max(x => x.Item1))
|
---|
411 | return true;
|
---|
412 | return Random.NextDouble() < ProbabilityAccept2dModel(scope.Fitness, adaptiveWalkPerformanceModel);
|
---|
413 | }
|
---|
414 | public bool AdaptivewalkingSuited(double startingFitness) {
|
---|
415 | if (adaptiveWalkPerformanceModel == null) return true;
|
---|
416 | if (startingFitness < AdaptivewalkingStat.Min(x => x.Item1) || startingFitness > AdaptivewalkingStat.Max(x => x.Item1))
|
---|
417 | return true;
|
---|
418 | return Random.NextDouble() < ProbabilityAccept2dModel(startingFitness, adaptiveWalkPerformanceModel);
|
---|
419 | }
|
---|
420 |
|
---|
421 | public IConfidenceRegressionSolution GetSolution(IConfidenceRegressionModel model, List<Tuple<double, double>> data) {
|
---|
422 | return new ConfidenceRegressionSolution(model, PrepareRegression(data));
|
---|
423 | }
|
---|
424 | public IConfidenceRegressionSolution GetSolution(IConfidenceRegressionModel model, List<Tuple<double, double, double>> data) {
|
---|
425 | return new ConfidenceRegressionSolution(model, PrepareRegression(data));
|
---|
426 | }
|
---|
427 |
|
---|
428 | protected RegressionProblemData PrepareRegression(List<Tuple<double, double>> sample) {
|
---|
429 | var inCol = new List<double>();
|
---|
430 | var outCol = new List<double>();
|
---|
431 | foreach (var next in sample.Shuffle(Random)) {
|
---|
432 | inCol.Add(next.Item1);
|
---|
433 | outCol.Add(next.Item2);
|
---|
434 | }
|
---|
435 | var ds = new Dataset(new[] { "in", "out" }, new[] { inCol, outCol });
|
---|
436 | var regPrb = new RegressionProblemData(ds, new[] { "in" }, "out") {
|
---|
437 | TrainingPartition = { Start = 0, End = Math.Min(50, sample.Count) },
|
---|
438 | TestPartition = { Start = Math.Min(50, sample.Count), End = sample.Count }
|
---|
439 | };
|
---|
440 | return regPrb;
|
---|
441 | }
|
---|
442 |
|
---|
443 | protected RegressionProblemData PrepareRegression(List<Tuple<double, double, double>> sample) {
|
---|
444 | var in1Col = new List<double>();
|
---|
445 | var in2Col = new List<double>();
|
---|
446 | var outCol = new List<double>();
|
---|
447 | foreach (var next in sample.Shuffle(Random)) {
|
---|
448 | in1Col.Add(next.Item1);
|
---|
449 | in2Col.Add(next.Item2);
|
---|
450 | outCol.Add(next.Item3);
|
---|
451 | }
|
---|
452 | var ds = new Dataset(new[] { "in1", "in2", "out" }, new[] { in1Col, in2Col, outCol });
|
---|
453 | var regPrb = new RegressionProblemData(ds, new[] { "in1", "in2" }, "out") {
|
---|
454 | TrainingPartition = { Start = 0, End = Math.Min(50, sample.Count) },
|
---|
455 | TestPartition = { Start = Math.Min(50, sample.Count), End = sample.Count }
|
---|
456 | };
|
---|
457 | return regPrb;
|
---|
458 | }
|
---|
459 |
|
---|
460 | protected static IConfidenceRegressionSolution RunRegression(RegressionProblemData trainingData, IConfidenceRegressionModel baseLineModel = null) {
|
---|
461 | var baseline = baseLineModel != null ? new ConfidenceRegressionSolution(baseLineModel, trainingData) : null;
|
---|
462 | var gpr = new GaussianProcessRegression { Problem = { ProblemData = trainingData } };
|
---|
463 | if (trainingData.InputVariables.CheckedItems.Any(x => alglib.pearsoncorr2(trainingData.Dataset.GetDoubleValues(x.Value.Value).ToArray(), trainingData.TargetVariableValues.ToArray()) > 0.8)) {
|
---|
464 | gpr.MeanFunction = new MeanZero();
|
---|
465 | var cov1 = new CovarianceSum();
|
---|
466 | cov1.Terms.Add(new CovarianceLinearArd());
|
---|
467 | cov1.Terms.Add(new CovarianceConst());
|
---|
468 | gpr.CovarianceFunction = cov1;
|
---|
469 | }
|
---|
470 | IConfidenceRegressionSolution solution = null;
|
---|
471 | var cnt = 0;
|
---|
472 | do {
|
---|
473 | ExecuteAlgorithm(gpr);
|
---|
474 | solution = (IConfidenceRegressionSolution)gpr.Results["Solution"].Value;
|
---|
475 | cnt++;
|
---|
476 | } while (cnt < 10 && (solution == null || solution.TrainingRSquared.IsAlmost(0)));
|
---|
477 | if (baseline == null) return solution;
|
---|
478 | if (trainingData.Dataset.Rows < 60)
|
---|
479 | return solution.TrainingMeanAbsoluteError < baseline.TrainingMeanAbsoluteError ? solution : baseline;
|
---|
480 | return solution.TestMeanAbsoluteError < baseline.TestMeanAbsoluteError ? solution : baseline;
|
---|
481 | }
|
---|
482 |
|
---|
483 | protected static void ExecuteAlgorithm(IAlgorithm algorithm) {
|
---|
484 | using (var evt = new AutoResetEvent(false)) {
|
---|
485 | EventHandler exeStateChanged = (o, args) => {
|
---|
486 | if (algorithm.ExecutionState == ExecutionState.Paused || algorithm.ExecutionState == ExecutionState.Stopped)
|
---|
487 | evt.Set();
|
---|
488 | };
|
---|
489 | algorithm.ExecutionStateChanged += exeStateChanged;
|
---|
490 | algorithm.Prepare(true);
|
---|
491 | algorithm.Start();
|
---|
492 | evt.WaitOne();
|
---|
493 | algorithm.ExecutionStateChanged -= exeStateChanged;
|
---|
494 | }
|
---|
495 | }
|
---|
496 |
|
---|
497 | protected double ProbabilityAccept(ISingleObjectiveSolutionScope<TSolution> scope, IList<Tuple<double, double>> data) {
|
---|
498 | if (double.IsNaN(scope.Fitness)) throw new ArgumentException("solution not evaluated or quality unknown", "scope");
|
---|
499 | return ProbabilityAccept2d(scope.Fitness, data);
|
---|
500 | }
|
---|
501 |
|
---|
502 | private double ProbabilityAccept2dModel(double a, IConfidenceRegressionModel model) {
|
---|
503 | var ds = new Dataset(new[] { "in", "out" }, new[] { new List<double> { a }, new List<double> { double.NaN } });
|
---|
504 | var mean = model.GetEstimatedValues(ds, new[] { 0 }).Single();
|
---|
505 | var sdev = Math.Sqrt(model.GetEstimatedVariances(ds, new[] { 0 }).Single());
|
---|
506 |
|
---|
507 | var goal = Problem.Maximization ? Population.Min(x => x.Fitness) : Population.Max(x => x.Fitness);
|
---|
508 | var z = (goal - mean) / sdev;
|
---|
509 | return Problem.Maximization ? 1.0 - Phi(z) /* P(X >= z) */ : Phi(z); // P(X <= z)
|
---|
510 | }
|
---|
511 | protected double ProbabilityAccept2d(double startingFitness, IList<Tuple<double, double>> data) {
|
---|
512 | if (data.Count < 10) return 1.0;
|
---|
513 | var samples = 0;
|
---|
514 | double meanStart = 0, meanStartOld = 0, meanEnd = 0, meanEndOld = 0;
|
---|
515 | double varStart = 0, varStartOld = 0, varEnd = 0, varEndOld = 0;
|
---|
516 | for (var i = 0; i < data.Count; i++) {
|
---|
517 | samples++;
|
---|
518 | var x = data[i].Item1;
|
---|
519 | var y = data[i].Item2;
|
---|
520 |
|
---|
521 | if (samples == 1) {
|
---|
522 | meanStartOld = x;
|
---|
523 | meanEndOld = y;
|
---|
524 | } else {
|
---|
525 | meanStart = meanStartOld + (x - meanStartOld) / samples;
|
---|
526 | meanEnd = meanEndOld + (y - meanEndOld) / samples;
|
---|
527 | varStart = varStartOld + (x - meanStartOld) * (x - meanStart) / (samples - 1);
|
---|
528 | varEnd = varEndOld + (y - meanEndOld) * (y - meanEnd) / (samples - 1);
|
---|
529 |
|
---|
530 | meanStartOld = meanStart;
|
---|
531 | meanEndOld = meanEnd;
|
---|
532 | varStartOld = varStart;
|
---|
533 | varEndOld = varEnd;
|
---|
534 | }
|
---|
535 | }
|
---|
536 | var cov = data.Select((v, i) => new { Index = i, Value = v }).Select(x => x.Value).Sum(x => (x.Item1 - meanStart) * (x.Item2 - meanEnd)) / data.Count;
|
---|
537 |
|
---|
538 | var biasedMean = meanEnd + cov / varStart * (startingFitness - meanStart);
|
---|
539 | var biasedStdev = Math.Sqrt(varEnd - (cov * cov) / varStart);
|
---|
540 |
|
---|
541 | if (Problem.Maximization) {
|
---|
542 | var goal = Population.Min(x => x.Fitness);
|
---|
543 | var z = (goal - biasedMean) / biasedStdev;
|
---|
544 | return 1.0 - Phi(z); // P(X >= z)
|
---|
545 | } else {
|
---|
546 | var goal = Population.Max(x => x.Fitness);
|
---|
547 | var z = (goal - biasedMean) / biasedStdev;
|
---|
548 | return Phi(z); // P(X <= z)
|
---|
549 | }
|
---|
550 | }
|
---|
551 |
|
---|
552 | private double ProbabilityAccept3dModel(double a, double b, IConfidenceRegressionModel model) {
|
---|
553 | var ds = new Dataset(new[] { "in1", "in2", "out" }, new[] { new List<double> { a }, new List<double> { b }, new List<double> { double.NaN } });
|
---|
554 | var mean = model.GetEstimatedValues(ds, new[] { 0 }).Single();
|
---|
555 | var sdev = Math.Sqrt(model.GetEstimatedVariances(ds, new[] { 0 }).Single());
|
---|
556 |
|
---|
557 | var goal = Problem.Maximization ? Population.Min(x => x.Fitness) : Population.Max(x => x.Fitness);
|
---|
558 | var z = (goal - mean) / sdev;
|
---|
559 | return Problem.Maximization ? 1.0 - Phi(z) /* P(X >= z) */ : Phi(z); // P(X <= z)
|
---|
560 | }
|
---|
561 | protected double ProbabilityAccept3d(double startingFitness1, double startingFitness2, IList<Tuple<double, double, double>> data) {
|
---|
562 | if (data.Count < 20) return 1.0;
|
---|
563 | var samples = 0;
|
---|
564 | double meanX1 = 0, meanX1Old = 0, meanX2 = 0, meanX2Old = 0, meanY = 0, meanYOld = 0;
|
---|
565 | double varX1 = 0, varX1Old = 0, varX2 = 0, varX2Old = 0, varY = 0, varYOld = 0;
|
---|
566 | for (var i = 0; i < data.Count; i++) {
|
---|
567 | samples++;
|
---|
568 | var x1 = data[i].Item1;
|
---|
569 | var x2 = data[i].Item2;
|
---|
570 | var y = data[i].Item3;
|
---|
571 |
|
---|
572 | if (samples == 1) {
|
---|
573 | meanX1Old = x1;
|
---|
574 | meanX2Old = x2;
|
---|
575 | meanYOld = y;
|
---|
576 | } else {
|
---|
577 | meanX1 = meanX1Old + (x1 - meanX1Old) / samples;
|
---|
578 | meanX2 = meanX2Old + (x2 - meanX2Old) / samples;
|
---|
579 | meanY = meanYOld + (y - meanYOld) / samples;
|
---|
580 | varX1 = varX1Old + (x1 - meanX1Old) * (x1 - meanX1) / (samples - 1);
|
---|
581 | varX2 = varX2Old + (x2 - meanX2Old) * (x2 - meanX2) / (samples - 1);
|
---|
582 | varY = varYOld + (y - meanYOld) * (y - meanY) / (samples - 1);
|
---|
583 |
|
---|
584 | meanX1Old = meanX1;
|
---|
585 | meanX2Old = meanX2;
|
---|
586 | meanYOld = meanY;
|
---|
587 | varX1Old = varX1;
|
---|
588 | varX2Old = varX2;
|
---|
589 | varYOld = varY;
|
---|
590 | }
|
---|
591 | }
|
---|
592 | double covX1X2 = 0, covX1Y = 0, covX2Y = 0;
|
---|
593 | for (var i = 0; i < data.Count; i++) {
|
---|
594 | covX1X2 += (data[i].Item1 - meanX1) * (data[i].Item2 - meanX2) / data.Count;
|
---|
595 | covX1Y += (data[i].Item1 - meanX1) * (data[i].Item3 - meanY) / data.Count;
|
---|
596 | covX2Y += (data[i].Item2 - meanX2) * (data[i].Item3 - meanY) / data.Count;
|
---|
597 | }
|
---|
598 |
|
---|
599 | var biasedMean = meanY + ((covX1Y * varX2 - covX2Y * covX1X2) * (startingFitness1 - meanX1) - (covX1Y * covX1X2 - covX2Y * varX1) * (startingFitness2 - meanX2)) / (varX1 * varX2 - covX1X2 * covX1X2);
|
---|
600 | var biasedStdev = Math.Sqrt(varY - (covX1Y * covX1Y * varX2 - 2 * covX1Y * covX2Y * covX1X2 + covX2Y * covX2Y * varX1) / (varX1 * varX2 - covX1X2 * covX1X2));
|
---|
601 | if (Problem.Maximization) {
|
---|
602 | var goal = Population.Min(x => x.Fitness);
|
---|
603 | var z = (goal - biasedMean) / biasedStdev;
|
---|
604 | return 1.0 - Phi(z); // P(X >= z)
|
---|
605 | } else {
|
---|
606 | var goal = Population.Max(x => x.Fitness);
|
---|
607 | var z = (goal - biasedMean) / biasedStdev;
|
---|
608 | return Phi(z); // P(X <= z)
|
---|
609 | }
|
---|
610 | }
|
---|
611 |
|
---|
612 | [MethodImpl(MethodImplOptions.AggressiveInlining)]
|
---|
613 | public bool IsBetter(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b) {
|
---|
614 | return IsBetter(a.Fitness, b.Fitness);
|
---|
615 | }
|
---|
616 | [MethodImpl(MethodImplOptions.AggressiveInlining)]
|
---|
617 | public bool IsBetter(double a, double b) {
|
---|
618 | return double.IsNaN(b) && !double.IsNaN(a)
|
---|
619 | || Problem.Maximization && a > b
|
---|
620 | || !Problem.Maximization && a < b;
|
---|
621 | }
|
---|
622 |
|
---|
623 | public void AddBreedingResult(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b, ISingleObjectiveSolutionScope<TSolution> child) {
|
---|
624 | if (IsBetter(a, b))
|
---|
625 | BreedingStat.Add(Tuple.Create(a.Fitness, b.Fitness, child.Fitness));
|
---|
626 | else BreedingStat.Add(Tuple.Create(b.Fitness, a.Fitness, child.Fitness));
|
---|
627 | }
|
---|
628 |
|
---|
629 | public void AddRelinkingResult(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b, ISingleObjectiveSolutionScope<TSolution> child) {
|
---|
630 | if (IsBetter(a, b))
|
---|
631 | RelinkingStat.Add(Tuple.Create(a.Fitness, b.Fitness, child.Fitness));
|
---|
632 | else RelinkingStat.Add(Tuple.Create(b.Fitness, a.Fitness, child.Fitness));
|
---|
633 | }
|
---|
634 |
|
---|
635 | public void AddDelinkingResult(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b, ISingleObjectiveSolutionScope<TSolution> child) {
|
---|
636 | if (IsBetter(a, b))
|
---|
637 | DelinkingStat.Add(Tuple.Create(a.Fitness, b.Fitness, child.Fitness));
|
---|
638 | else DelinkingStat.Add(Tuple.Create(b.Fitness, a.Fitness, child.Fitness));
|
---|
639 | }
|
---|
640 |
|
---|
641 | public void AddSamplingResult(ISingleObjectiveSolutionScope<TSolution> sample) {
|
---|
642 | SamplingStat.Add(Tuple.Create(Population.Average(x => x.Fitness), sample.Fitness));
|
---|
643 | }
|
---|
644 |
|
---|
645 | public void AddHillclimbingResult(ISingleObjectiveSolutionScope<TSolution> input, ISingleObjectiveSolutionScope<TSolution> outcome) {
|
---|
646 | HillclimbingStat.Add(Tuple.Create(input.Fitness, outcome.Fitness));
|
---|
647 | }
|
---|
648 |
|
---|
649 | public void AddTabuwalkingResult(ISingleObjectiveSolutionScope<TSolution> input, ISingleObjectiveSolutionScope<TSolution> outcome) {
|
---|
650 | AdaptivewalkingStat.Add(Tuple.Create(input.Fitness, outcome.Fitness));
|
---|
651 | }
|
---|
652 |
|
---|
653 | #region IExecutionContext members
|
---|
654 | public IAtomicOperation CreateOperation(IOperator op) {
|
---|
655 | return new ExecutionContext(this, op, Scope);
|
---|
656 | }
|
---|
657 |
|
---|
658 | public IAtomicOperation CreateOperation(IOperator op, IScope s) {
|
---|
659 | return new ExecutionContext(this, op, s);
|
---|
660 | }
|
---|
661 |
|
---|
662 | public IAtomicOperation CreateChildOperation(IOperator op) {
|
---|
663 | return new ExecutionContext(this, op, Scope);
|
---|
664 | }
|
---|
665 |
|
---|
666 | public IAtomicOperation CreateChildOperation(IOperator op, IScope s) {
|
---|
667 | return new ExecutionContext(this, op, s);
|
---|
668 | }
|
---|
669 | #endregion
|
---|
670 |
|
---|
671 | #region Math Helper
|
---|
672 | // normal distribution CDF (left of x) for N(0;1) standard normal distribution
|
---|
673 | // from http://www.johndcook.com/blog/csharp_phi/
|
---|
674 | // license: "This code is in the public domain. Do whatever you want with it, no strings attached."
|
---|
675 | // added: 2016-11-19 21:46 CET
|
---|
676 | protected static double Phi(double x) {
|
---|
677 | // constants
|
---|
678 | double a1 = 0.254829592;
|
---|
679 | double a2 = -0.284496736;
|
---|
680 | double a3 = 1.421413741;
|
---|
681 | double a4 = -1.453152027;
|
---|
682 | double a5 = 1.061405429;
|
---|
683 | double p = 0.3275911;
|
---|
684 |
|
---|
685 | // Save the sign of x
|
---|
686 | int sign = 1;
|
---|
687 | if (x < 0)
|
---|
688 | sign = -1;
|
---|
689 | x = Math.Abs(x) / Math.Sqrt(2.0);
|
---|
690 |
|
---|
691 | // A&S formula 7.1.26
|
---|
692 | double t = 1.0 / (1.0 + p * x);
|
---|
693 | double y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * Math.Exp(-x * x);
|
---|
694 |
|
---|
695 | return 0.5 * (1.0 + sign * y);
|
---|
696 | }
|
---|
697 | #endregion
|
---|
698 | }
|
---|
699 |
|
---|
700 | [Item("SingleSolutionMemPRContext", "Abstract base class for single solution MemPR contexts.")]
|
---|
701 | [StorableClass]
|
---|
702 | public abstract class MemPRSolutionContext<TProblem, TSolution, TContext, TSolutionContext> : ParameterizedNamedItem,
|
---|
703 | ISingleSolutionHeuristicAlgorithmContext<TProblem, TSolution>
|
---|
704 | where TProblem : class, IItem, ISingleObjectiveProblemDefinition
|
---|
705 | where TSolution : class, IItem
|
---|
706 | where TContext : MemPRPopulationContext<TProblem, TSolution, TContext, TSolutionContext>
|
---|
707 | where TSolutionContext : MemPRSolutionContext<TProblem, TSolution, TContext, TSolutionContext> {
|
---|
708 |
|
---|
709 | private TContext parent;
|
---|
710 | public IExecutionContext Parent {
|
---|
711 | get { return parent; }
|
---|
712 | set { throw new InvalidOperationException("Cannot set the parent of a single-solution context."); }
|
---|
713 | }
|
---|
714 |
|
---|
715 | [Storable]
|
---|
716 | private ISingleObjectiveSolutionScope<TSolution> scope;
|
---|
717 | public IScope Scope {
|
---|
718 | get { return scope; }
|
---|
719 | }
|
---|
720 |
|
---|
721 | IKeyedItemCollection<string, IParameter> IExecutionContext.Parameters {
|
---|
722 | get { return Parameters; }
|
---|
723 | }
|
---|
724 |
|
---|
725 | public TProblem Problem {
|
---|
726 | get { return parent.Problem; }
|
---|
727 | }
|
---|
728 |
|
---|
729 | public double BestQuality {
|
---|
730 | get { return parent.BestQuality; }
|
---|
731 | set { parent.BestQuality = value; }
|
---|
732 | }
|
---|
733 |
|
---|
734 | public TSolution BestSolution {
|
---|
735 | get { return parent.BestSolution; }
|
---|
736 | set { parent.BestSolution = value; }
|
---|
737 | }
|
---|
738 |
|
---|
739 | public IRandom Random {
|
---|
740 | get { return parent.Random; }
|
---|
741 | }
|
---|
742 |
|
---|
743 | [Storable]
|
---|
744 | private IValueParameter<IntValue> evaluatedSolutions;
|
---|
745 | public int EvaluatedSolutions {
|
---|
746 | get { return evaluatedSolutions.Value.Value; }
|
---|
747 | set { evaluatedSolutions.Value.Value = value; }
|
---|
748 | }
|
---|
749 |
|
---|
750 | [Storable]
|
---|
751 | private IValueParameter<IntValue> iterations;
|
---|
752 | public int Iterations {
|
---|
753 | get { return iterations.Value.Value; }
|
---|
754 | set { iterations.Value.Value = value; }
|
---|
755 | }
|
---|
756 |
|
---|
757 | ISingleObjectiveSolutionScope<TSolution> ISingleSolutionHeuristicAlgorithmContext<TProblem, TSolution>.Solution {
|
---|
758 | get { return scope; }
|
---|
759 | }
|
---|
760 |
|
---|
761 | [StorableConstructor]
|
---|
762 | protected MemPRSolutionContext(bool deserializing) : base(deserializing) { }
|
---|
763 | protected MemPRSolutionContext(MemPRSolutionContext<TProblem, TSolution, TContext, TSolutionContext> original, Cloner cloner)
|
---|
764 | : base(original, cloner) {
|
---|
765 | scope = cloner.Clone(original.scope);
|
---|
766 | evaluatedSolutions = cloner.Clone(original.evaluatedSolutions);
|
---|
767 | iterations = cloner.Clone(original.iterations);
|
---|
768 | }
|
---|
769 | public MemPRSolutionContext(TContext baseContext, ISingleObjectiveSolutionScope<TSolution> solution) {
|
---|
770 | parent = baseContext;
|
---|
771 | scope = solution;
|
---|
772 |
|
---|
773 | Parameters.Add(evaluatedSolutions = new ValueParameter<IntValue>("EvaluatedSolutions", new IntValue(0)));
|
---|
774 | Parameters.Add(iterations = new ValueParameter<IntValue>("Iterations", new IntValue(0)));
|
---|
775 | }
|
---|
776 |
|
---|
777 | public void IncrementEvaluatedSolutions(int byEvaluations) {
|
---|
778 | if (byEvaluations < 0) throw new ArgumentException("Can only increment and not decrement evaluated solutions.");
|
---|
779 | EvaluatedSolutions += byEvaluations;
|
---|
780 | }
|
---|
781 |
|
---|
782 | #region IExecutionContext members
|
---|
783 | public IAtomicOperation CreateOperation(IOperator op) {
|
---|
784 | return new ExecutionContext(this, op, Scope);
|
---|
785 | }
|
---|
786 |
|
---|
787 | public IAtomicOperation CreateOperation(IOperator op, IScope s) {
|
---|
788 | return new ExecutionContext(this, op, s);
|
---|
789 | }
|
---|
790 |
|
---|
791 | public IAtomicOperation CreateChildOperation(IOperator op) {
|
---|
792 | return new ExecutionContext(this, op, Scope);
|
---|
793 | }
|
---|
794 |
|
---|
795 | public IAtomicOperation CreateChildOperation(IOperator op, IScope s) {
|
---|
796 | return new ExecutionContext(this, op, s);
|
---|
797 | }
|
---|
798 | #endregion
|
---|
799 | }
|
---|
800 | }
|
---|