1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2013 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.Text;
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26 | using System.Threading.Tasks;
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27 | using HeuristicLab.Analysis;
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28 | using HeuristicLab.Common;
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29 | using HeuristicLab.Core;
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30 | using HeuristicLab.Data;
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31 | using HeuristicLab.Encodings.BinaryVectorEncoding;
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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.Random;
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36 |
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37 | namespace HeuristicLab.Algorithms.ParameterlessPopulationPyramid {
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38 | [Item("Parameter-less Population Pyramid", "Binary value optimization algorithm which requires no configuration.")]
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39 | [StorableClass]
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40 | [Creatable("Parameterless Population Pyramid")]
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41 |
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42 | public class ParameterlessPopulationPyramid : AlgorithmBase {
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43 | private readonly IRandom random = new MersenneTwister();
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44 | private List<Population> pyramid;
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45 | private EvaluationTracker tracker;
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46 |
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47 | // Tracks all solutions in Pyramid for quick membership checks
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48 | private HashSet<bool[]> seen = new HashSet<bool[]>(new EnumerableBoolEqualityComparer());
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49 |
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50 | #region ParameterNames
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51 | private const string MaximumIterationsParameterName = "Maximum Iterations";
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52 | private const string MaximumEvaluationsParameterName = "Maximum Evaluations";
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53 | private const string SeedParameterName = "Seed";
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54 | private const string SetSeedRandomlyParameterName = "SetSeedRandomly";
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55 | #endregion
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56 |
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57 | #region ParameterProperties
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58 | public IFixedValueParameter<IntValue> MaximumIterationsParameter {
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59 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumIterationsParameterName]; }
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60 | }
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61 | public IFixedValueParameter<IntValue> MaximumEvaluationsParameter {
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62 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumEvaluationsParameterName]; }
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63 | }
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64 | public IFixedValueParameter<IntValue> SeedParameter {
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65 | get { return (IFixedValueParameter<IntValue>)Parameters[SeedParameterName]; }
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66 | }
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67 | public FixedValueParameter<BoolValue> SetSeedRandomlyParameter {
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68 | get { return (FixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyParameterName]; }
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69 | }
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70 | #endregion
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71 |
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72 | #region Properties
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73 | public int MaximumIterations {
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74 | get { return MaximumIterationsParameter.Value.Value; }
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75 | set { MaximumIterationsParameter.Value.Value = value; }
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76 | }
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77 |
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78 | public int MaximumEvaluations {
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79 | get { return MaximumEvaluationsParameter.Value.Value; }
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80 | set { MaximumEvaluationsParameter.Value.Value = value; }
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81 | }
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82 |
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83 | public int Seed {
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84 | get { return SeedParameter.Value.Value; }
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85 | set { SeedParameter.Value.Value = value; }
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86 | }
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87 |
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88 | public bool SetSeedRandomly {
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89 | get { return SetSeedRandomlyParameter.Value.Value; }
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90 | set { SetSeedRandomlyParameter.Value.Value = value; }
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91 | }
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92 | #endregion
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93 |
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94 | #region ResultsProperties
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95 | private double ResultsBestQuality {
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96 | get { return ((DoubleValue)Results["Best Quality"].Value).Value; }
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97 | set { ((DoubleValue)Results["Best Quality"].Value).Value = value; }
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98 | }
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99 |
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100 | private BinaryVector ResultsBestSolution {
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101 | get { return (BinaryVector)Results["Best Solution"].Value; }
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102 | set { Results["Best Solution"].Value = value; }
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103 | }
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104 |
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105 | private int ResultsBestFoundOnEvaluation {
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106 | get { return ((IntValue)Results["Evaluation Best Solution Was Found"].Value).Value; }
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107 | set { ((IntValue)Results["Evaluation Best Solution Was Found"].Value).Value = value; }
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108 | }
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109 |
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110 | private int ResultsEvaluations {
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111 | get { return ((IntValue)Results["Evaluations"].Value).Value; }
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112 | set { ((IntValue)Results["Evaluations"].Value).Value = value; }
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113 | }
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114 | private int ResultsIterations {
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115 | get { return ((IntValue)Results["Iterations"].Value).Value; }
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116 | set { ((IntValue)Results["Iterations"].Value).Value = value; }
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117 | }
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118 |
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119 | private DataTable ResultsQualities {
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120 | get { return ((DataTable)Results["Qualities"].Value); }
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121 | }
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122 | private DataRow ResultsQualitiesBest {
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123 | get { return ResultsQualities.Rows["Best Quality"]; }
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124 | }
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125 |
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126 | private DataRow ResultsQualitiesIteration {
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127 | get { return ResultsQualities.Rows["Iteration Quality"]; }
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128 | }
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129 |
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130 |
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131 | private DataRow ResultsLevels {
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132 | get { return ((DataTable)Results["Pyramid Levels"].Value).Rows["Levels"]; }
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133 | }
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134 |
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135 | private DataRow ResultsSolutions {
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136 | get { return ((DataTable)Results["Stored Solutions"].Value).Rows["Solutions"]; }
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137 | }
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138 | #endregion
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139 |
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140 | [StorableConstructor]
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141 | protected ParameterlessPopulationPyramid(bool deserializing) : base(deserializing) { }
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142 |
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143 | protected ParameterlessPopulationPyramid(ParameterlessPopulationPyramid original, Cloner cloner)
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144 | : base(original, cloner) {
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145 | }
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146 |
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147 | public override IDeepCloneable Clone(Cloner cloner) {
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148 | return new ParameterlessPopulationPyramid(this, cloner);
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149 | }
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150 |
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151 | public ParameterlessPopulationPyramid() {
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152 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumIterationsParameterName, "", new IntValue(Int32.MaxValue)));
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153 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumEvaluationsParameterName, "", new IntValue(10000)));
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154 | 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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155 | 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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156 | }
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157 |
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158 | private void AddIfUnique(bool[] solution, int level) {
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159 | // Don't add things you have seen
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160 | if (seen.Contains(solution)) return;
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161 | if (level == pyramid.Count) {
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162 | pyramid.Add(new Population(tracker.Length, random));
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163 | }
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164 | var copied = (bool[])solution.Clone();
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165 | pyramid[level].Add(copied);
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166 | seen.Add(copied);
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167 | }
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168 |
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169 | // In the GECCO paper, Figure 1
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170 | private double iterate() {
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171 | // Create a random solution
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172 | bool[] solution = new bool[tracker.Length];
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173 | for (int i = 0; i < solution.Length; i++) {
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174 | solution[i] = random.Next(2) == 1;
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175 | }
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176 | double fitness = tracker.Evaluate(solution);
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177 | fitness = HillClimber.ImproveToLocalOptimum(tracker, solution, fitness, random);
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178 | AddIfUnique(solution, 0);
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179 |
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180 | for (int level = 0; level < pyramid.Count; level++) {
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181 | var current = pyramid[level];
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182 | double newFitness = LinkageCrossover.ImproveUsingTree(current.Tree, current.Solutions, solution, fitness, tracker, random);
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183 | // add it to the next level if its a strict fitness improvement
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184 | if (tracker.IsBetter(newFitness, fitness)) {
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185 | fitness = newFitness;
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186 | AddIfUnique(solution, level + 1);
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187 | }
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188 | }
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189 | return fitness;
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190 | }
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191 |
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192 | protected override void Run() {
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193 | // Set up the algorithm
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194 | if (SetSeedRandomly) Seed = new System.Random().Next();
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195 | pyramid = new List<Population>();
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196 | seen.Clear();
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197 | random.Reset(Seed);
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198 | tracker = new EvaluationTracker(Problem, MaximumEvaluations);
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199 |
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200 | // Set up the results display
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201 | Results.Add(new Result("Iterations", new IntValue(0)));
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202 | Results.Add(new Result("Evaluations", new IntValue(0)));
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203 | Results.Add(new Result("Best Solution", new BinaryVector(tracker.BestSolution)));
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204 | Results.Add(new Result("Best Quality", new DoubleValue(tracker.BestQuality)));
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205 | Results.Add(new Result("Evaluation Best Solution Was Found", new IntValue(tracker.BestFoundOnEvaluation)));
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206 | var table = new DataTable("Qualities");
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207 | table.Rows.Add(new DataRow("Best Quality"));
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208 | var iterationRows = new DataRow("Iteration Quality");
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209 | iterationRows.VisualProperties.LineStyle = DataRowVisualProperties.DataRowLineStyle.Dot;
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210 | table.Rows.Add(iterationRows);
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211 | Results.Add(new Result("Qualities", table));
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212 |
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213 | table = new DataTable("Pyramid Levels");
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214 | table.Rows.Add(new DataRow("Levels"));
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215 | Results.Add(new Result("Pyramid Levels", table));
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216 |
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217 | table = new DataTable("Stored Solutions");
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218 | table.Rows.Add(new DataRow("Solutions"));
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219 | Results.Add(new Result("Stored Solutions", table));
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220 |
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221 | // Loop until iteration limit reached or canceled.
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222 | for (ResultsIterations = 0; ResultsIterations < MaximumIterations; ResultsIterations++) {
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223 | double fitness = double.NaN;
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224 |
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225 | try {
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226 | fitness = iterate();
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227 | }
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228 | catch (OperationCanceledException) {
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229 | throw;
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230 | }
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231 | finally {
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232 | ResultsEvaluations = tracker.Evaluations;
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233 | ResultsBestSolution = new BinaryVector(tracker.BestSolution);
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234 | ResultsBestQuality = tracker.BestQuality;
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235 | ResultsBestFoundOnEvaluation = tracker.BestFoundOnEvaluation;
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236 | ResultsQualitiesBest.Values.Add(tracker.BestQuality);
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237 | ResultsQualitiesIteration.Values.Add(fitness);
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238 | ResultsLevels.Values.Add(pyramid.Count);
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239 | ResultsSolutions.Values.Add(seen.Count);
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240 | }
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241 | }
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242 | }
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243 | }
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244 | }
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