source: branches/2994-AutoDiffForIntervals/HeuristicLab.Algorithms.CMAEvolutionStrategy/3.4/CMAOperators/CMALogweightedRecombinator.cs @ 17209

Last change on this file since 17209 was 17209, checked in by gkronber, 4 months ago

#2994: merged r17132:17198 from trunk to branch

File size: 1.9 KB
Line 
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
22using System;
23using System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HEAL.Attic;
27
28namespace HeuristicLab.Algorithms.CMAEvolutionStrategy {
29  [Item("CMA Log-weighted Recombinator", "Calculates weighted mean based on a logarithmic decreasing weights.")]
30  [StorableType("83A47E81-FF87-4A28-AEB4-DE453B55B680")]
31  public class CMALogweightedRecombinator : CMARecombinator {
32
33    [StorableConstructor]
34    protected CMALogweightedRecombinator(StorableConstructorFlag _) : base(_) { }
35    protected CMALogweightedRecombinator(CMALogweightedRecombinator original, Cloner cloner) : base(original, cloner) { }
36    public CMALogweightedRecombinator() : base() { }
37
38    public override IDeepCloneable Clone(Cloner cloner) {
39      return new CMALogweightedRecombinator(this, cloner);
40    }
41
42    protected override double[] GetWeights(int mu) {
43      var weights = new double[mu];
44      for (int i = 0; i < mu; i++) weights[i] = Math.Log((mu + 1.0) / (i + 1.0));
45      var sum = weights.Sum();
46      for (int i = 0; i < mu; i++) weights[i] /= sum;
47      return weights;
48    }
49  }
50}
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