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.Threading;
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26 | using HeuristicLab.Algorithms.MemPR.Interfaces;
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27 | using HeuristicLab.Algorithms.MemPR.Util;
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28 | using HeuristicLab.Collections;
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29 | using HeuristicLab.Common;
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30 | using HeuristicLab.Core;
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31 | using HeuristicLab.Encodings.LinearLinkageEncoding;
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32 | using HeuristicLab.Optimization;
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33 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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34 | using HeuristicLab.PluginInfrastructure;
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35 | using HeuristicLab.Random;
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36 |
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37 | namespace HeuristicLab.Algorithms.MemPR.LinearLinkage {
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38 | [Item("MemPR (linear linkage)", "MemPR implementation for linear linkage vectors.")]
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39 | [StorableClass]
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40 | [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 999)]
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41 | public class LinearLinkageMemPR : MemPRAlgorithm<SingleObjectiveBasicProblem<LinearLinkageEncoding>, Encodings.LinearLinkageEncoding.LinearLinkage, LinearLinkageMemPRPopulationContext, LinearLinkageMemPRSolutionContext> {
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42 | private const double UncommonBitSubsetMutationProbabilityMagicConst = 0.05;
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43 |
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44 | [StorableConstructor]
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45 | protected LinearLinkageMemPR(bool deserializing) : base(deserializing) { }
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46 | protected LinearLinkageMemPR(LinearLinkageMemPR original, Cloner cloner) : base(original, cloner) { }
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47 | public LinearLinkageMemPR() {
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48 | foreach (var trainer in ApplicationManager.Manager.GetInstances<ISolutionModelTrainer<LinearLinkageMemPRPopulationContext>>())
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49 | SolutionModelTrainerParameter.ValidValues.Add(trainer);
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50 |
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51 | foreach (var localSearch in ApplicationManager.Manager.GetInstances<ILocalSearch<LinearLinkageMemPRSolutionContext>>()) {
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52 | LocalSearchParameter.ValidValues.Add(localSearch);
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53 | }
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54 | }
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55 |
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56 | public override IDeepCloneable Clone(Cloner cloner) {
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57 | return new LinearLinkageMemPR(this, cloner);
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58 | }
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59 |
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60 | protected override bool Eq(ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage> a, ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage> b) {
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61 | var s1 = a.Solution;
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62 | var s2 = b.Solution;
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63 | if (s1.Length != s2.Length) return false;
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64 | for (var i = 0; i < s1.Length; i++)
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65 | if (s1[i] != s2[i]) return false;
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66 | return true;
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67 | }
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68 |
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69 | protected override double Dist(ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage> a, ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage> b) {
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70 | return HammingSimilarityCalculator.CalculateSimilarity(a.Solution, b.Solution);
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71 | }
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72 |
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73 | protected override ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage> ToScope(Encodings.LinearLinkageEncoding.LinearLinkage code, double fitness = double.NaN) {
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74 | var creator = Problem.SolutionCreator as ILinearLinkageCreator;
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75 | if (creator == null) throw new InvalidOperationException("Can only solve linear linkage encoded problems with MemPR (linear linkage)");
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76 | return new SingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage>(code, creator.LLEParameter.ActualName, fitness, Problem.Evaluator.QualityParameter.ActualName) {
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77 | Parent = Context.Scope
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78 | };
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79 | }
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80 |
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81 | protected override ISolutionSubspace<Encodings.LinearLinkageEncoding.LinearLinkage> CalculateSubspace(IEnumerable<Encodings.LinearLinkageEncoding.LinearLinkage> solutions, bool inverse = false) {
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82 | var pop = solutions.ToList();
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83 | var N = pop[0].Length;
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84 | var subspace = new bool[N];
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85 | for (var i = 0; i < N; i++) {
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86 | var val = pop[0][i];
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87 | if (inverse) subspace[i] = true;
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88 | for (var p = 1; p < pop.Count; p++) {
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89 | if (pop[p][i] != val) subspace[i] = !inverse;
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90 | }
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91 | }
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92 | return new LinearLinkageSolutionSubspace(subspace);
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93 | }
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94 |
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95 | protected override int TabuWalk(
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96 | ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage> scope,
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97 | int maxEvals, CancellationToken token,
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98 | ISolutionSubspace<Encodings.LinearLinkageEncoding.LinearLinkage> sub_space = null) {
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99 | var maximization = Context.Problem.Maximization;
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100 | var subspace = sub_space is LinearLinkageSolutionSubspace ? ((LinearLinkageSolutionSubspace)sub_space).Subspace : null;
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101 | var evaluations = 0;
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102 | var quality = scope.Fitness;
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103 | var bestQuality = quality;
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104 | if (double.IsNaN(quality)) {
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105 | Evaluate(scope, token);
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106 | quality = scope.Fitness;
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107 | evaluations++;
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108 | if (evaluations >= maxEvals) return evaluations;
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109 | }
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110 | var currentScope = (ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage>)scope.Clone();
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111 | var current = currentScope.Solution;
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112 |
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113 | var tabu = new double[current.Length, current.Length];
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114 | for (var i = 0; i < current.Length; i++) {
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115 | for (var j = i; j < current.Length; j++) {
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116 | tabu[i, j] = tabu[j, i] = maximization ? double.MinValue : double.MaxValue;
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117 | }
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118 | tabu[i, current[i]] = quality;
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119 | }
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120 |
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121 | // this dictionary holds the last relevant links
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122 | var links = new BidirectionalDictionary<int, int>();
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123 | bool allMoveTabu;
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124 | for (var iter = 0; iter < int.MaxValue; iter++) {
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125 | allMoveTabu = true;
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126 | // clear the dictionary before a new pass through the array is made
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127 | links.Clear();
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128 | for (var i = 0; i < current.Length; i++) {
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129 | if (subspace != null && !subspace[i]) {
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130 | links.RemoveBySecond(i);
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131 | links.Add(i, current[i]);
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132 | continue;
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133 | }
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134 |
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135 | var next = current[i];
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136 | foreach (var move in MoveGenerator.GenerateForItem(i, next, links)) {
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137 | // we intend to break link i -> next
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138 | var qualityToBreak = tabu[i, next];
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139 | move.Apply(current);
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140 | var qualityToRestore = tabu[i, current[i]]; // current[i] is new next
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141 | Evaluate(currentScope, token);
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142 | evaluations++;
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143 | var moveF = currentScope.Fitness;
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144 | var isNotTabu = FitnessComparer.IsBetter(maximization, moveF, qualityToBreak)
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145 | && FitnessComparer.IsBetter(maximization, moveF, qualityToRestore);
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146 | if (isNotTabu) allMoveTabu = false;
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147 | var isImprovement = FitnessComparer.IsBetter(maximization, moveF, bestQuality);
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148 | if (isNotTabu || isImprovement) {
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149 | if (maximization) {
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150 | tabu[i, next] = Math.Max(tabu[i, next], moveF);
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151 | tabu[i, current[i]] = Math.Max(tabu[i, current[i]], moveF);
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152 | } else {
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153 | tabu[i, next] = Math.Min(tabu[i, next], moveF);
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154 | tabu[i, current[i]] = Math.Min(tabu[i, current[i]], moveF);
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155 | }
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156 | quality = moveF;
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157 | if (isImprovement) bestQuality = quality;
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158 |
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159 | move.UpdateLinks(links);
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160 | break;
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161 | } else move.Undo(current);
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162 | if (evaluations >= maxEvals) break;
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163 | }
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164 | links.RemoveBySecond(i);
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165 | links.Add(i, current[i]);
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166 | if (evaluations >= maxEvals) break;
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167 | if (token.IsCancellationRequested) break;
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168 | }
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169 | if (allMoveTabu) break;
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170 | if (evaluations >= maxEvals) break;
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171 | if (token.IsCancellationRequested) break;
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172 | }
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173 | return evaluations;
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174 | }
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175 |
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176 | protected override ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage> Cross(ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage> p1Scope, ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage> p2Scope, CancellationToken token) {
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177 | var p1 = p1Scope.Solution;
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178 | var p2 = p2Scope.Solution;
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179 | var transfered = new bool[p1.Length];
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180 | var subspace = new bool[p1.Length];
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181 | var lleeChild = new int[p1.Length];
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182 | var lleep1 = p1.ToLLEe();
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183 | var lleep2 = p2.ToLLEe();
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184 | for (var i = p1.Length - 1; i >= 0; i--) {
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185 | // Step 1
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186 | subspace[i] = p1[i] != p2[i];
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187 | var p1IsEnd = p1[i] == i;
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188 | var p2IsEnd = p2[i] == i;
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189 | if (p1IsEnd & p2IsEnd) {
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190 | transfered[i] = true;
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191 | } else if (p1IsEnd | p2IsEnd) {
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192 | transfered[i] = Context.Random.NextDouble() < 0.5;
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193 | }
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194 | lleeChild[i] = i;
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195 |
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196 | // Step 2
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197 | if (transfered[i]) continue;
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198 | var end1 = lleep1[i];
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199 | var end2 = lleep2[i];
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200 | var containsEnd1 = transfered[end1];
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201 | var containsEnd2 = transfered[end2];
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202 | if (containsEnd1 & containsEnd2) {
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203 | if (Context.Random.NextDouble() < 0.5) {
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204 | lleeChild[i] = end1;
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205 | } else {
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206 | lleeChild[i] = end2;
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207 | }
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208 | } else if (containsEnd1) {
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209 | lleeChild[i] = end1;
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210 | } else if (containsEnd2) {
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211 | lleeChild[i] = end2;
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212 | } else {
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213 | if (Context.Random.NextDouble() < 0.5) {
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214 | lleeChild[i] = lleeChild[p1[i]];
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215 | } else {
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216 | lleeChild[i] = lleeChild[p2[i]];
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217 | }
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218 | }
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219 | }
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220 | var child = new Encodings.LinearLinkageEncoding.LinearLinkage(lleeChild.Length);
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221 | child.FromLLEe(lleeChild);
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222 |
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223 | return ToScope(child);
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224 | }
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225 |
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226 | protected override void Mutate(ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage> offspring, CancellationToken token, ISolutionSubspace<Encodings.LinearLinkageEncoding.LinearLinkage> subspace = null) {
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227 | var lle = offspring.Solution;
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228 | var subset = subspace is LinearLinkageSolutionSubspace ? ((LinearLinkageSolutionSubspace)subspace).Subspace : null;
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229 | for (var i = 0; i < lle.Length - 1; i++) {
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230 | if (subset == null || subset[i]) continue; // mutation works against crossover so aims to mutate noTouch points
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231 | if (Context.Random.NextDouble() < UncommonBitSubsetMutationProbabilityMagicConst) {
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232 | subset[i] = true;
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233 | var index = Context.Random.Next(i, lle.Length);
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234 | for (var j = index - 1; j >= i; j--) {
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235 | if (lle[j] == index) index = j;
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236 | }
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237 | lle[i] = index;
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238 | index = i;
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239 | var idx2 = i;
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240 | for (var j = i - 1; j >= 0; j--) {
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241 | if (lle[j] == lle[index]) {
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242 | lle[j] = idx2;
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243 | index = idx2 = j;
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244 | } else if (lle[j] == idx2) idx2 = j;
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245 | }
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246 | }
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247 | }
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248 | }
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249 |
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250 | protected override ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage> Relink(ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage> a, ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage> b, CancellationToken token) {
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251 | var maximization = Context.Problem.Maximization;
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252 | if (double.IsNaN(a.Fitness)) {
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253 | Evaluate(a, token);
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254 | Context.IncrementEvaluatedSolutions(1);
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255 | }
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256 | if (double.IsNaN(b.Fitness)) {
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257 | Evaluate(b, token);
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258 | Context.IncrementEvaluatedSolutions(1);
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259 | }
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260 | var child = (ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage>)a.Clone();
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261 | var cgroups = child.Solution.GetGroups().Select(x => new HashSet<int>(x)).ToList();
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262 | var g2 = b.Solution.GetGroups().ToList();
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263 | var order = Enumerable.Range(0, g2.Count).Shuffle(Context.Random).ToList();
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264 | ISingleObjectiveSolutionScope <Encodings.LinearLinkageEncoding.LinearLinkage> bestChild = null;
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265 | for (var j = 0; j < g2.Count; j++) {
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266 | var g = g2[order[j]];
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267 | var changed = false;
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268 | for (var k = j; k < cgroups.Count; k++) {
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269 | foreach (var f in g) if (cgroups[k].Remove(f)) changed = true;
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270 | if (cgroups[k].Count == 0) {
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271 | cgroups.RemoveAt(k);
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272 | k--;
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273 | }
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274 | }
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275 | cgroups.Insert(0, new HashSet<int>(g));
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276 | child.Solution.SetGroups(cgroups);
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277 | if (changed) {
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278 | Evaluate(child, token);
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279 | if (bestChild == null || FitnessComparer.IsBetter(maximization, child.Fitness, bestChild.Fitness)) {
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280 | bestChild = (ISingleObjectiveSolutionScope<Encodings.LinearLinkageEncoding.LinearLinkage>)child.Clone();
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281 | }
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282 | }
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283 | };
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284 | return bestChild;
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285 | }
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286 | }
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287 | }
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