1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 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 HeuristicLab.Common;


26  using HeuristicLab.Core;


27  using HEAL.Attic;


28  using HeuristicLab.Random;


29 


30  namespace HeuristicLab.Encodings.LinearLinkageEncoding {


31  [Item("Lowest Index Max Crossover", "The Lowest Index Max Crossover (LIMX) is implemented as described in Ülker, Ö., Özcan, 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. 347363. Springer Berlin Heidelberg.")]


32  [StorableType("CDD944699B804FBBAC40DADE1A9462F1")]


33  public sealed class LowestIndexMaxCrossover : LinearLinkageCrossover {


34 


35  [StorableConstructor]


36  private LowestIndexMaxCrossover(StorableConstructorFlag _) : base(_) { }


37  private LowestIndexMaxCrossover(LowestIndexMaxCrossover original, Cloner cloner) : base(original, cloner) { }


38  public LowestIndexMaxCrossover() { }


39 


40  public override IDeepCloneable Clone(Cloner cloner) {


41  return new LowestIndexMaxCrossover(this, cloner);


42  }


43 


44  public static LinearLinkage Apply(IRandom random, ItemArray<LinearLinkage> parents) {


45  var len = parents[0].Length;


46  var child = LinearLinkage.SingleElementGroups(len);


47  var remaining = new SortedSet<int>(Enumerable.Range(0, len));


48  do {


49  var groups = parents.Select(x => x.GetGroupForward(remaining.Min).Where(y => remaining.Contains(y)).ToList()).ToList();


50  var max = groups.Select((v, idx) => Tuple.Create(idx, v.Count)).MaxItems(x => x.Item2).SampleRandom(random).Item1;


51  var i = groups[max][0];


52  for (var k = 1; k < groups[max].Count; k++) {


53  child[i] = groups[max][k];


54  remaining.Remove(i);


55  i = child[i];


56  }


57  child[i] = i;


58  remaining.Remove(i);


59  } while (remaining.Count > 0);


60 


61  return child;


62  }


63 


64  protected override LinearLinkage Cross(IRandom random, ItemArray<LinearLinkage> parents) {


65  return Apply(random, parents);


66  }


67  }


68  }

