1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2015 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 HeuristicLab.Common;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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28 | using HeuristicLab.Random;
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29 |
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30 | namespace HeuristicLab.Encodings.LinearLinkageEncoding {
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31 | [Item("Greedy Partition Crossover", "The Greedy Partition Crossover (GPX) is implemented as described in Ãlker, Ã., Ãzcan, E., Korkmaz, E. E. 2007. Linear linkage encoding in grouping problems: applications on graph coloring and timetabling. In Practice and Theory of Automated Timetabling VI, pp. 347-363. Springer Berlin Heidelberg.")]
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32 | [StorableClass("81824B06-5123-4339-9D67-E19DCEC7FAC8")]
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33 | public sealed class GreedyPartitionCrossover : LinearLinkageCrossover {
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34 |
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35 | [StorableConstructor]
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36 | private GreedyPartitionCrossover(bool deserializing) : base(deserializing) { }
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37 | private GreedyPartitionCrossover(GreedyPartitionCrossover original, Cloner cloner) : base(original, cloner) { }
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38 | public GreedyPartitionCrossover() { }
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39 |
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40 | public override IDeepCloneable Clone(Cloner cloner) {
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41 | return new GreedyPartitionCrossover(this, cloner);
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42 | }
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43 |
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44 | public static LinearLinkage Apply(IRandom random, ItemArray<LinearLinkage> parents) {
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45 | var len = parents[0].Length;
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46 |
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47 | var child = new LinearLinkage(len);
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48 | var childGroup = new List<HashSet<int>>();
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49 | var currentParent = random.Next(parents.Length);
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50 | var groups = parents.Select(x => x.GetGroups().Select(y => new HashSet<int>(y)).ToList()).ToList();
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51 | bool remaining;
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52 | do {
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53 | var maxGroup = groups[currentParent].Select((v, i) => Tuple.Create(i, v))
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54 | .MaxItems(x => x.Item2.Count)
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55 | .SampleRandom(random).Item1;
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56 | var group = groups[currentParent][maxGroup];
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57 | groups[currentParent].RemoveAt(maxGroup);
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58 | childGroup.Add(group);
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59 |
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60 | remaining = false;
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61 | for (var p = 0; p < groups.Count; p++) {
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62 | for (var j = 0; j < groups[p].Count; j++) {
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63 | foreach (var elem in group) groups[p][j].Remove(elem);
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64 | if (!remaining && groups[p][j].Count > 0) remaining = true;
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65 | }
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66 | }
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67 |
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68 | currentParent = (currentParent + 1) % parents.Length;
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69 | } while (remaining);
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70 |
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71 | child.SetGroups(childGroup);
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72 | return child;
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73 | }
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74 |
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75 | protected override LinearLinkage Cross(IRandom random, ItemArray<LinearLinkage> parents) {
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76 | return Apply(random, parents);
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77 | }
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78 | }
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79 | }
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