[14420] | 1 | #region License Information
|
---|
| 2 | /* HeuristicLab
|
---|
| 3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
| 4 | *
|
---|
| 5 | * This file is part of HeuristicLab.
|
---|
| 6 | *
|
---|
| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
|
---|
| 8 | * it under the terms of the GNU General Public License as published by
|
---|
| 9 | * the Free Software Foundation, either version 3 of the License, or
|
---|
| 10 | * (at your option) any later version.
|
---|
| 11 | *
|
---|
| 12 | * HeuristicLab is distributed in the hope that it will be useful,
|
---|
| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
|
---|
| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
---|
| 15 | * GNU General Public License for more details.
|
---|
| 16 | *
|
---|
| 17 | * You should have received a copy of the GNU General Public License
|
---|
| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
|
---|
| 19 | */
|
---|
| 20 | #endregion
|
---|
| 21 |
|
---|
| 22 | using System;
|
---|
| 23 | using System.Collections.Generic;
|
---|
| 24 | using System.Linq;
|
---|
| 25 | using System.Threading;
|
---|
[14450] | 26 | using HeuristicLab.Algorithms.MemPR.Interfaces;
|
---|
[14420] | 27 | using HeuristicLab.Common;
|
---|
| 28 | using HeuristicLab.Core;
|
---|
| 29 | using HeuristicLab.Encodings.BinaryVectorEncoding;
|
---|
| 30 | using HeuristicLab.Optimization;
|
---|
| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
| 32 | using HeuristicLab.PluginInfrastructure;
|
---|
| 33 | using HeuristicLab.Random;
|
---|
| 34 |
|
---|
| 35 | namespace HeuristicLab.Algorithms.MemPR.Binary {
|
---|
| 36 | [Item("MemPR (binary)", "MemPR implementation for binary vectors.")]
|
---|
| 37 | [StorableClass]
|
---|
| 38 | [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 999)]
|
---|
[14450] | 39 | public class BinaryMemPR : MemPRAlgorithm<SingleObjectiveBasicProblem<BinaryVectorEncoding>, BinaryVector, BinaryMemPRPopulationContext, BinaryMemPRSolutionContext> {
|
---|
[14420] | 40 | private const double UncommonBitSubsetMutationProbabilityMagicConst = 0.05;
|
---|
| 41 |
|
---|
| 42 | [StorableConstructor]
|
---|
| 43 | protected BinaryMemPR(bool deserializing) : base(deserializing) { }
|
---|
| 44 | protected BinaryMemPR(BinaryMemPR original, Cloner cloner) : base(original, cloner) { }
|
---|
| 45 | public BinaryMemPR() {
|
---|
[14450] | 46 | foreach (var trainer in ApplicationManager.Manager.GetInstances<ISolutionModelTrainer<BinaryMemPRPopulationContext>>())
|
---|
[14420] | 47 | SolutionModelTrainerParameter.ValidValues.Add(trainer);
|
---|
| 48 |
|
---|
[14450] | 49 | foreach (var localSearch in ApplicationManager.Manager.GetInstances<ILocalSearch<BinaryMemPRSolutionContext>>()) {
|
---|
| 50 | LocalSearchParameter.ValidValues.Add(localSearch);
|
---|
[14420] | 51 | }
|
---|
| 52 | }
|
---|
| 53 |
|
---|
| 54 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
| 55 | return new BinaryMemPR(this, cloner);
|
---|
| 56 | }
|
---|
| 57 |
|
---|
| 58 | protected override bool Eq(ISingleObjectiveSolutionScope<BinaryVector> a, ISingleObjectiveSolutionScope<BinaryVector> b) {
|
---|
| 59 | var len = a.Solution.Length;
|
---|
| 60 | var acode = a.Solution;
|
---|
| 61 | var bcode = b.Solution;
|
---|
| 62 | for (var i = 0; i < len; i++)
|
---|
| 63 | if (acode[i] != bcode[i]) return false;
|
---|
| 64 | return true;
|
---|
| 65 | }
|
---|
| 66 |
|
---|
| 67 | protected override double Dist(ISingleObjectiveSolutionScope<BinaryVector> a, ISingleObjectiveSolutionScope<BinaryVector> b) {
|
---|
| 68 | var len = a.Solution.Length;
|
---|
| 69 | var acode = a.Solution;
|
---|
| 70 | var bcode = b.Solution;
|
---|
| 71 | var hamming = 0;
|
---|
| 72 | for (var i = 0; i < len; i++)
|
---|
| 73 | if (acode[i] != bcode[i]) hamming++;
|
---|
| 74 | return hamming / (double)len;
|
---|
| 75 | }
|
---|
| 76 |
|
---|
| 77 | protected override ISingleObjectiveSolutionScope<BinaryVector> ToScope(BinaryVector code, double fitness = double.NaN) {
|
---|
| 78 | var creator = Problem.SolutionCreator as IBinaryVectorCreator;
|
---|
| 79 | if (creator == null) throw new InvalidOperationException("Can only solve binary encoded problems with MemPR (binary)");
|
---|
| 80 | return new SingleObjectiveSolutionScope<BinaryVector>(code, creator.BinaryVectorParameter.ActualName, fitness, Problem.Evaluator.QualityParameter.ActualName) {
|
---|
| 81 | Parent = Context.Scope
|
---|
| 82 | };
|
---|
| 83 | }
|
---|
| 84 |
|
---|
[14450] | 85 | protected override ISolutionSubspace<BinaryVector> CalculateSubspace(IEnumerable<BinaryVector> solutions, bool inverse = false) {
|
---|
[14420] | 86 | var pop = solutions.ToList();
|
---|
| 87 | var N = pop[0].Length;
|
---|
| 88 | var subspace = new bool[N];
|
---|
| 89 | for (var i = 0; i < N; i++) {
|
---|
| 90 | var val = pop[0][i];
|
---|
| 91 | if (inverse) subspace[i] = true;
|
---|
| 92 | for (var p = 1; p < pop.Count; p++) {
|
---|
| 93 | if (pop[p][i] != val) subspace[i] = !inverse;
|
---|
| 94 | }
|
---|
| 95 | }
|
---|
| 96 | return new BinarySolutionSubspace(subspace);
|
---|
| 97 | }
|
---|
| 98 |
|
---|
[14450] | 99 | protected override void TabuWalk(ISingleObjectiveSolutionScope<BinaryVector> scope, int steps, CancellationToken token, ISolutionSubspace<BinaryVector> subspace = null) {
|
---|
[14453] | 100 | var evaluations = 0;
|
---|
[14420] | 101 | var subset = subspace != null ? ((BinarySolutionSubspace)subspace).Subspace : null;
|
---|
[14453] | 102 | if (double.IsNaN(scope.Fitness)) {
|
---|
| 103 | Evaluate(scope, token);
|
---|
| 104 | evaluations++;
|
---|
| 105 | }
|
---|
[14420] | 106 | SingleObjectiveSolutionScope<BinaryVector> bestOfTheWalk = null;
|
---|
| 107 | var currentScope = (SingleObjectiveSolutionScope<BinaryVector>)scope.Clone();
|
---|
| 108 | var current = currentScope.Solution;
|
---|
| 109 | var N = current.Length;
|
---|
| 110 | var tabu = new Tuple<double, double>[N];
|
---|
| 111 | for (var i = 0; i < N; i++) tabu[i] = Tuple.Create(current[i] ? double.NaN : currentScope.Fitness, !current[i] ? double.NaN : currentScope.Fitness);
|
---|
| 112 | var subN = subset != null ? subset.Count(x => x) : N;
|
---|
| 113 | if (subN == 0) return;
|
---|
| 114 | var order = Enumerable.Range(0, N).Where(x => subset == null || subset[x]).Shuffle(Context.Random).ToArray();
|
---|
| 115 |
|
---|
| 116 | for (var iter = 0; iter < steps; iter++) {
|
---|
| 117 | var allTabu = true;
|
---|
| 118 | var bestOfTheRestF = double.NaN;
|
---|
| 119 | int bestOfTheRest = -1;
|
---|
| 120 | var improved = false;
|
---|
| 121 |
|
---|
| 122 | for (var i = 0; i < subN; i++) {
|
---|
| 123 | var idx = order[i];
|
---|
| 124 | var before = currentScope.Fitness;
|
---|
| 125 | current[idx] = !current[idx];
|
---|
| 126 | Evaluate(currentScope, token);
|
---|
[14453] | 127 | evaluations++;
|
---|
[14420] | 128 | var after = currentScope.Fitness;
|
---|
| 129 |
|
---|
| 130 | if (IsBetter(after, before) && (bestOfTheWalk == null || IsBetter(after, bestOfTheWalk.Fitness))) {
|
---|
| 131 | bestOfTheWalk = (SingleObjectiveSolutionScope<BinaryVector>)currentScope.Clone();
|
---|
| 132 | }
|
---|
| 133 |
|
---|
| 134 | var qualityToBeat = current[idx] ? tabu[idx].Item2 : tabu[idx].Item1;
|
---|
| 135 | var isTabu = !IsBetter(after, qualityToBeat);
|
---|
| 136 | if (!isTabu) allTabu = false;
|
---|
| 137 |
|
---|
| 138 | if (IsBetter(after, before) && !isTabu) {
|
---|
| 139 | improved = true;
|
---|
| 140 | tabu[idx] = current[idx] ? Tuple.Create(after, tabu[idx].Item2) : Tuple.Create(tabu[idx].Item1, after);
|
---|
| 141 | } else { // undo the move
|
---|
| 142 | if (!isTabu && IsBetter(after, bestOfTheRestF)) {
|
---|
| 143 | bestOfTheRest = idx;
|
---|
| 144 | bestOfTheRestF = after;
|
---|
| 145 | }
|
---|
| 146 | current[idx] = !current[idx];
|
---|
| 147 | currentScope.Fitness = before;
|
---|
| 148 | }
|
---|
| 149 | }
|
---|
| 150 | if (!allTabu && !improved) {
|
---|
| 151 | var better = currentScope.Fitness;
|
---|
| 152 | current[bestOfTheRest] = !current[bestOfTheRest];
|
---|
| 153 | tabu[bestOfTheRest] = current[bestOfTheRest] ? Tuple.Create(better, tabu[bestOfTheRest].Item2) : Tuple.Create(tabu[bestOfTheRest].Item1, better);
|
---|
| 154 | currentScope.Fitness = bestOfTheRestF;
|
---|
| 155 | } else if (allTabu) break;
|
---|
| 156 | }
|
---|
| 157 |
|
---|
[14453] | 158 | Context.IncrementEvaluatedSolutions(evaluations);
|
---|
[14420] | 159 | scope.Adopt(bestOfTheWalk ?? currentScope);
|
---|
| 160 | }
|
---|
| 161 |
|
---|
| 162 | protected override ISingleObjectiveSolutionScope<BinaryVector> Cross(ISingleObjectiveSolutionScope<BinaryVector> p1, ISingleObjectiveSolutionScope<BinaryVector> p2, CancellationToken token) {
|
---|
| 163 | var offspring = (ISingleObjectiveSolutionScope<BinaryVector>)p1.Clone();
|
---|
| 164 | offspring.Fitness = double.NaN;
|
---|
| 165 | var code = offspring.Solution;
|
---|
| 166 | var p2Code = p2.Solution;
|
---|
| 167 | var bp = 0;
|
---|
| 168 | var lastbp = 0;
|
---|
| 169 | for (var i = 0; i < code.Length; i++) {
|
---|
| 170 | if (bp % 2 == 1) {
|
---|
| 171 | code[i] = p2Code[i];
|
---|
| 172 | }
|
---|
[14450] | 173 | if (Context.Random.Next(code.Length) < i - lastbp + 1) {
|
---|
[14420] | 174 | bp = (bp + 1) % 2;
|
---|
| 175 | lastbp = i;
|
---|
| 176 | }
|
---|
| 177 | }
|
---|
| 178 | return offspring;
|
---|
| 179 | }
|
---|
| 180 |
|
---|
[14450] | 181 | protected override void Mutate(ISingleObjectiveSolutionScope<BinaryVector> offspring, CancellationToken token, ISolutionSubspace<BinaryVector> subspace = null) {
|
---|
[14420] | 182 | var subset = subspace != null ? ((BinarySolutionSubspace)subspace).Subspace : null;
|
---|
| 183 | offspring.Fitness = double.NaN;
|
---|
| 184 | var code = offspring.Solution;
|
---|
| 185 | for (var i = 0; i < code.Length; i++) {
|
---|
| 186 | if (subset != null && subset[i]) continue;
|
---|
| 187 | if (Context.Random.NextDouble() < UncommonBitSubsetMutationProbabilityMagicConst) {
|
---|
| 188 | code[i] = !code[i];
|
---|
| 189 | if (subset != null) subset[i] = true;
|
---|
| 190 | }
|
---|
| 191 | }
|
---|
| 192 | }
|
---|
| 193 |
|
---|
| 194 | protected override ISingleObjectiveSolutionScope<BinaryVector> Relink(ISingleObjectiveSolutionScope<BinaryVector> a, ISingleObjectiveSolutionScope<BinaryVector> b, CancellationToken token) {
|
---|
[14453] | 195 | if (double.IsNaN(a.Fitness)) {
|
---|
| 196 | Evaluate(a, token);
|
---|
| 197 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 198 | }
|
---|
| 199 | if (double.IsNaN(b.Fitness)) {
|
---|
| 200 | Evaluate(b, token);
|
---|
| 201 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 202 | }
|
---|
[14420] | 203 | if (Context.Random.NextDouble() < 0.5)
|
---|
| 204 | return IsBetter(a, b) ? Relink(a, b, token, false) : Relink(b, a, token, true);
|
---|
| 205 | else return IsBetter(a, b) ? Relink(b, a, token, true) : Relink(a, b, token, false);
|
---|
| 206 | }
|
---|
| 207 |
|
---|
| 208 | protected virtual ISingleObjectiveSolutionScope<BinaryVector> Relink(ISingleObjectiveSolutionScope<BinaryVector> betterScope, ISingleObjectiveSolutionScope<BinaryVector> worseScope, CancellationToken token, bool fromWorseToBetter) {
|
---|
[14453] | 209 | var evaluations = 0;
|
---|
[14420] | 210 | var childScope = (ISingleObjectiveSolutionScope<BinaryVector>)(fromWorseToBetter ? worseScope : betterScope).Clone();
|
---|
| 211 | var child = childScope.Solution;
|
---|
| 212 | var better = betterScope.Solution;
|
---|
| 213 | var worse = worseScope.Solution;
|
---|
| 214 | ISingleObjectiveSolutionScope<BinaryVector> best = null;
|
---|
| 215 | var cF = fromWorseToBetter ? worseScope.Fitness : betterScope.Fitness;
|
---|
| 216 | var bF = double.NaN;
|
---|
| 217 | var order = Enumerable.Range(0, better.Length).Shuffle(Context.Random).ToArray();
|
---|
| 218 | while (true) {
|
---|
| 219 | var bestS = double.NaN;
|
---|
| 220 | var bestIdx = -1;
|
---|
| 221 | for (var i = 0; i < child.Length; i++) {
|
---|
| 222 | var idx = order[i];
|
---|
| 223 | // either move from worse to better or move from better away from worse
|
---|
| 224 | if (fromWorseToBetter && child[idx] == better[idx] ||
|
---|
| 225 | !fromWorseToBetter && child[idx] != worse[idx]) continue;
|
---|
| 226 | child[idx] = !child[idx]; // move
|
---|
| 227 | Evaluate(childScope, token);
|
---|
[14453] | 228 | evaluations++;
|
---|
[14420] | 229 | var s = childScope.Fitness;
|
---|
| 230 | childScope.Fitness = cF;
|
---|
| 231 | child[idx] = !child[idx]; // undo move
|
---|
| 232 | if (IsBetter(s, cF)) {
|
---|
| 233 | bestS = s;
|
---|
| 234 | bestIdx = idx;
|
---|
| 235 | break; // first-improvement
|
---|
| 236 | }
|
---|
| 237 | if (double.IsNaN(bestS) || IsBetter(s, bestS)) {
|
---|
| 238 | // least-degrading
|
---|
| 239 | bestS = s;
|
---|
| 240 | bestIdx = idx;
|
---|
| 241 | }
|
---|
| 242 | }
|
---|
| 243 | if (bestIdx < 0) break;
|
---|
| 244 | child[bestIdx] = !child[bestIdx];
|
---|
| 245 | cF = bestS;
|
---|
| 246 | childScope.Fitness = cF;
|
---|
| 247 | if (IsBetter(cF, bF)) {
|
---|
| 248 | bF = cF;
|
---|
| 249 | best = (ISingleObjectiveSolutionScope<BinaryVector>)childScope.Clone();
|
---|
| 250 | }
|
---|
| 251 | }
|
---|
[14453] | 252 | Context.IncrementEvaluatedSolutions(evaluations);
|
---|
[14420] | 253 | return best ?? childScope;
|
---|
| 254 | }
|
---|
| 255 | }
|
---|
| 256 | }
|
---|