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source: branches/Async/HeuristicLab.Algorithms.CMAEvolutionStrategy/3.4/CMAOperators/CMALinearweightedRecombinator.cs @ 14544

Last change on this file since 14544 was 12012, checked in by ascheibe, 10 years ago

#2212 merged r12008, r12009, r12010 back into trunk

File size: 1.9 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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
22using HeuristicLab.Common;
23using HeuristicLab.Core;
24using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
25
26namespace HeuristicLab.Algorithms.CMAEvolutionStrategy {
27  [Item("CMA Linear-weighted Recombinator", "Calculates weighted mean using linear decreasing weights.")]
28  [StorableClass]
29  public class CMALinearweightedRecombinator : CMARecombinator {
30
31    [StorableConstructor]
32    protected CMALinearweightedRecombinator(bool deserializing) : base(deserializing) { }
33    protected CMALinearweightedRecombinator(CMALinearweightedRecombinator original, Cloner cloner) : base(original, cloner) { }
34    public CMALinearweightedRecombinator() : base() { }
35
36    public override IDeepCloneable Clone(Cloner cloner) {
37      return new CMALinearweightedRecombinator(this, cloner);
38    }
39
40    protected override double[] GetWeights(int mu) {
41      var weights = new double[mu];
42      var sum = (mu + 1) * mu / 2.0; // sum of arithmetic progression mu..1
43      for (int i = 0; i < mu; i++)
44        weights[i] = (mu - i) / sum;
45      return weights;
46    }
47  }
48}
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