source: branches/2990_VariableImpactBasedFeatureSelection/HeuristicLab.Encodings.LinearLinkageEncoding/3.4/Crossovers/LowestIndexMaxCrossover.cs @ 18140

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
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 * GNU General Public License for more details.
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#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HEAL.Attic;
using HeuristicLab.Random;

namespace HeuristicLab.Encodings.LinearLinkageEncoding {
  [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. 347-363. Springer Berlin Heidelberg.")]
  [StorableType("CDD94469-9B80-4FBB-AC40-DADE1A9462F1")]
  public sealed class LowestIndexMaxCrossover : LinearLinkageCrossover {

    [StorableConstructor]
    private LowestIndexMaxCrossover(StorableConstructorFlag _) : base(_) { }
    private LowestIndexMaxCrossover(LowestIndexMaxCrossover original, Cloner cloner) : base(original, cloner) { }
    public LowestIndexMaxCrossover() { }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new LowestIndexMaxCrossover(this, cloner);
    }

    public static LinearLinkage Apply(IRandom random, ItemArray<LinearLinkage> parents) {
      var len = parents[0].Length;
      var child = LinearLinkage.SingleElementGroups(len);
      var remaining = new SortedSet<int>(Enumerable.Range(0, len));
      do {
        var groups = parents.Select(x => x.GetGroupForward(remaining.Min).Where(y => remaining.Contains(y)).ToList()).ToList();
        var max = groups.Select((v, idx) => Tuple.Create(idx, v.Count)).MaxItems(x => x.Item2).SampleRandom(random).Item1;
        var i = groups[max][0];
        for (var k = 1; k < groups[max].Count; k++) {
          child[i] = groups[max][k];
          remaining.Remove(i);
          i = child[i];
        }
        child[i] = i;
        remaining.Remove(i);
      } while (remaining.Count > 0);

      return child;
    }

    protected override LinearLinkage Cross(IRandom random, ItemArray<LinearLinkage> parents) {
      return Apply(random, parents);
    }
  }
}