[14420] | 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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[14450] | 26 | using HeuristicLab.Algorithms.MemPR.Interfaces;
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[14420] | 27 | using HeuristicLab.Common;
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| 28 | using HeuristicLab.Core;
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| 29 | using HeuristicLab.Encodings.BinaryVectorEncoding;
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| 30 | using HeuristicLab.Optimization;
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| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 32 | using HeuristicLab.PluginInfrastructure;
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| 33 | using HeuristicLab.Random;
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| 34 |
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| 35 | namespace HeuristicLab.Algorithms.MemPR.Binary {
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| 36 | [Item("MemPR (binary)", "MemPR implementation for binary vectors.")]
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| 37 | [StorableClass]
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| 38 | [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 999)]
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[14450] | 39 | public class BinaryMemPR : MemPRAlgorithm<SingleObjectiveBasicProblem<BinaryVectorEncoding>, BinaryVector, BinaryMemPRPopulationContext, BinaryMemPRSolutionContext> {
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[14420] | 40 | private const double UncommonBitSubsetMutationProbabilityMagicConst = 0.05;
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| 41 |
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| 42 | [StorableConstructor]
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| 43 | protected BinaryMemPR(bool deserializing) : base(deserializing) { }
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| 44 | protected BinaryMemPR(BinaryMemPR original, Cloner cloner) : base(original, cloner) { }
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| 45 | public BinaryMemPR() {
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[14450] | 46 | foreach (var trainer in ApplicationManager.Manager.GetInstances<ISolutionModelTrainer<BinaryMemPRPopulationContext>>())
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[14420] | 47 | SolutionModelTrainerParameter.ValidValues.Add(trainer);
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| 48 |
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[14450] | 49 | foreach (var localSearch in ApplicationManager.Manager.GetInstances<ILocalSearch<BinaryMemPRSolutionContext>>()) {
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| 50 | LocalSearchParameter.ValidValues.Add(localSearch);
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[14420] | 51 | }
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| 52 | }
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| 53 |
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| 54 | public override IDeepCloneable Clone(Cloner cloner) {
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| 55 | return new BinaryMemPR(this, cloner);
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| 56 | }
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| 57 |
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| 58 | protected override bool Eq(ISingleObjectiveSolutionScope<BinaryVector> a, ISingleObjectiveSolutionScope<BinaryVector> b) {
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| 59 | var len = a.Solution.Length;
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| 60 | var acode = a.Solution;
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| 61 | var bcode = b.Solution;
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| 62 | for (var i = 0; i < len; i++)
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| 63 | if (acode[i] != bcode[i]) return false;
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| 64 | return true;
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| 65 | }
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| 66 |
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| 67 | protected override double Dist(ISingleObjectiveSolutionScope<BinaryVector> a, ISingleObjectiveSolutionScope<BinaryVector> b) {
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| 68 | var len = a.Solution.Length;
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| 69 | var acode = a.Solution;
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| 70 | var bcode = b.Solution;
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| 71 | var hamming = 0;
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| 72 | for (var i = 0; i < len; i++)
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| 73 | if (acode[i] != bcode[i]) hamming++;
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| 74 | return hamming / (double)len;
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| 75 | }
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| 76 |
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| 77 | protected override ISingleObjectiveSolutionScope<BinaryVector> ToScope(BinaryVector code, double fitness = double.NaN) {
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| 78 | var creator = Problem.SolutionCreator as IBinaryVectorCreator;
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| 79 | if (creator == null) throw new InvalidOperationException("Can only solve binary encoded problems with MemPR (binary)");
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| 80 | return new SingleObjectiveSolutionScope<BinaryVector>(code, creator.BinaryVectorParameter.ActualName, fitness, Problem.Evaluator.QualityParameter.ActualName) {
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| 81 | Parent = Context.Scope
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| 82 | };
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| 83 | }
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| 84 |
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[14450] | 85 | protected override ISolutionSubspace<BinaryVector> CalculateSubspace(IEnumerable<BinaryVector> solutions, bool inverse = false) {
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[14420] | 86 | var pop = solutions.ToList();
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| 87 | var N = pop[0].Length;
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| 88 | var subspace = new bool[N];
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| 89 | for (var i = 0; i < N; i++) {
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| 90 | var val = pop[0][i];
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| 91 | if (inverse) subspace[i] = true;
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| 92 | for (var p = 1; p < pop.Count; p++) {
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| 93 | if (pop[p][i] != val) subspace[i] = !inverse;
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| 94 | }
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| 95 | }
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| 96 | return new BinarySolutionSubspace(subspace);
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| 97 | }
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| 98 |
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[14450] | 99 | protected override void TabuWalk(ISingleObjectiveSolutionScope<BinaryVector> scope, int steps, CancellationToken token, ISolutionSubspace<BinaryVector> subspace = null) {
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[14420] | 100 | var subset = subspace != null ? ((BinarySolutionSubspace)subspace).Subspace : null;
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| 101 | if (double.IsNaN(scope.Fitness)) Evaluate(scope, token);
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| 102 | SingleObjectiveSolutionScope<BinaryVector> bestOfTheWalk = null;
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| 103 | var currentScope = (SingleObjectiveSolutionScope<BinaryVector>)scope.Clone();
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| 104 | var current = currentScope.Solution;
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| 105 | var N = current.Length;
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| 106 | var tabu = new Tuple<double, double>[N];
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| 107 | for (var i = 0; i < N; i++) tabu[i] = Tuple.Create(current[i] ? double.NaN : currentScope.Fitness, !current[i] ? double.NaN : currentScope.Fitness);
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| 108 | var subN = subset != null ? subset.Count(x => x) : N;
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| 109 | if (subN == 0) return;
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| 110 | var order = Enumerable.Range(0, N).Where(x => subset == null || subset[x]).Shuffle(Context.Random).ToArray();
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| 111 |
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| 112 | for (var iter = 0; iter < steps; iter++) {
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| 113 | var allTabu = true;
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| 114 | var bestOfTheRestF = double.NaN;
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| 115 | int bestOfTheRest = -1;
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| 116 | var improved = false;
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| 117 |
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| 118 | for (var i = 0; i < subN; i++) {
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| 119 | var idx = order[i];
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| 120 | var before = currentScope.Fitness;
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| 121 | current[idx] = !current[idx];
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| 122 | Evaluate(currentScope, token);
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| 123 | var after = currentScope.Fitness;
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| 124 |
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| 125 | if (IsBetter(after, before) && (bestOfTheWalk == null || IsBetter(after, bestOfTheWalk.Fitness))) {
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| 126 | bestOfTheWalk = (SingleObjectiveSolutionScope<BinaryVector>)currentScope.Clone();
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| 127 | }
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| 128 |
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| 129 | var qualityToBeat = current[idx] ? tabu[idx].Item2 : tabu[idx].Item1;
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| 130 | var isTabu = !IsBetter(after, qualityToBeat);
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| 131 | if (!isTabu) allTabu = false;
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| 132 |
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| 133 | if (IsBetter(after, before) && !isTabu) {
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| 134 | improved = true;
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| 135 | tabu[idx] = current[idx] ? Tuple.Create(after, tabu[idx].Item2) : Tuple.Create(tabu[idx].Item1, after);
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| 136 | } else { // undo the move
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| 137 | if (!isTabu && IsBetter(after, bestOfTheRestF)) {
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| 138 | bestOfTheRest = idx;
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| 139 | bestOfTheRestF = after;
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| 140 | }
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| 141 | current[idx] = !current[idx];
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| 142 | currentScope.Fitness = before;
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| 143 | }
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| 144 | }
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| 145 | if (!allTabu && !improved) {
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| 146 | var better = currentScope.Fitness;
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| 147 | current[bestOfTheRest] = !current[bestOfTheRest];
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| 148 | tabu[bestOfTheRest] = current[bestOfTheRest] ? Tuple.Create(better, tabu[bestOfTheRest].Item2) : Tuple.Create(tabu[bestOfTheRest].Item1, better);
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| 149 | currentScope.Fitness = bestOfTheRestF;
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| 150 | } else if (allTabu) break;
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| 151 | }
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| 152 |
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| 153 | scope.Adopt(bestOfTheWalk ?? currentScope);
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| 154 | }
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| 155 |
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| 156 | protected override ISingleObjectiveSolutionScope<BinaryVector> Cross(ISingleObjectiveSolutionScope<BinaryVector> p1, ISingleObjectiveSolutionScope<BinaryVector> p2, CancellationToken token) {
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| 157 | var offspring = (ISingleObjectiveSolutionScope<BinaryVector>)p1.Clone();
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| 158 | offspring.Fitness = double.NaN;
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| 159 | var code = offspring.Solution;
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| 160 | var p2Code = p2.Solution;
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| 161 | var bp = 0;
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| 162 | var lastbp = 0;
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| 163 | for (var i = 0; i < code.Length; i++) {
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| 164 | if (bp % 2 == 1) {
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| 165 | code[i] = p2Code[i];
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| 166 | }
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[14450] | 167 | if (Context.Random.Next(code.Length) < i - lastbp + 1) {
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[14420] | 168 | bp = (bp + 1) % 2;
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| 169 | lastbp = i;
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| 170 | }
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| 171 | }
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| 172 | return offspring;
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| 173 | }
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| 174 |
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[14450] | 175 | protected override void Mutate(ISingleObjectiveSolutionScope<BinaryVector> offspring, CancellationToken token, ISolutionSubspace<BinaryVector> subspace = null) {
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[14420] | 176 | var subset = subspace != null ? ((BinarySolutionSubspace)subspace).Subspace : null;
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| 177 | offspring.Fitness = double.NaN;
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| 178 | var code = offspring.Solution;
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| 179 | for (var i = 0; i < code.Length; i++) {
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| 180 | if (subset != null && subset[i]) continue;
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| 181 | if (Context.Random.NextDouble() < UncommonBitSubsetMutationProbabilityMagicConst) {
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| 182 | code[i] = !code[i];
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| 183 | if (subset != null) subset[i] = true;
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| 184 | }
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| 185 | }
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| 186 | }
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| 187 |
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| 188 | protected override ISingleObjectiveSolutionScope<BinaryVector> Relink(ISingleObjectiveSolutionScope<BinaryVector> a, ISingleObjectiveSolutionScope<BinaryVector> b, CancellationToken token) {
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| 189 | if (double.IsNaN(a.Fitness)) Evaluate(a, token);
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| 190 | if (double.IsNaN(b.Fitness)) Evaluate(b, token);
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| 191 | if (Context.Random.NextDouble() < 0.5)
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| 192 | return IsBetter(a, b) ? Relink(a, b, token, false) : Relink(b, a, token, true);
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| 193 | else return IsBetter(a, b) ? Relink(b, a, token, true) : Relink(a, b, token, false);
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| 194 | }
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| 195 |
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| 196 | protected virtual ISingleObjectiveSolutionScope<BinaryVector> Relink(ISingleObjectiveSolutionScope<BinaryVector> betterScope, ISingleObjectiveSolutionScope<BinaryVector> worseScope, CancellationToken token, bool fromWorseToBetter) {
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| 197 | var childScope = (ISingleObjectiveSolutionScope<BinaryVector>)(fromWorseToBetter ? worseScope : betterScope).Clone();
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| 198 | var child = childScope.Solution;
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| 199 | var better = betterScope.Solution;
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| 200 | var worse = worseScope.Solution;
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| 201 | ISingleObjectiveSolutionScope<BinaryVector> best = null;
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| 202 | var cF = fromWorseToBetter ? worseScope.Fitness : betterScope.Fitness;
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| 203 | var bF = double.NaN;
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| 204 | var order = Enumerable.Range(0, better.Length).Shuffle(Context.Random).ToArray();
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| 205 | while (true) {
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| 206 | var bestS = double.NaN;
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| 207 | var bestIdx = -1;
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| 208 | for (var i = 0; i < child.Length; i++) {
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| 209 | var idx = order[i];
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| 210 | // either move from worse to better or move from better away from worse
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| 211 | if (fromWorseToBetter && child[idx] == better[idx] ||
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| 212 | !fromWorseToBetter && child[idx] != worse[idx]) continue;
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| 213 | child[idx] = !child[idx]; // move
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| 214 | Evaluate(childScope, token);
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| 215 | var s = childScope.Fitness;
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| 216 | childScope.Fitness = cF;
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| 217 | child[idx] = !child[idx]; // undo move
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| 218 | if (IsBetter(s, cF)) {
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| 219 | bestS = s;
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| 220 | bestIdx = idx;
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| 221 | break; // first-improvement
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| 222 | }
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| 223 | if (double.IsNaN(bestS) || IsBetter(s, bestS)) {
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| 224 | // least-degrading
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| 225 | bestS = s;
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| 226 | bestIdx = idx;
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| 227 | }
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| 228 | }
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| 229 | if (bestIdx < 0) break;
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| 230 | child[bestIdx] = !child[bestIdx];
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| 231 | cF = bestS;
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| 232 | childScope.Fitness = cF;
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| 233 | if (IsBetter(cF, bF)) {
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| 234 | bF = cF;
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| 235 | best = (ISingleObjectiveSolutionScope<BinaryVector>)childScope.Clone();
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| 236 | }
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| 237 | }
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| 238 | return best ?? childScope;
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| 239 | }
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| 240 | }
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| 241 | }
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