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source: stable/HeuristicLab.Algorithms.CMAEvolutionStrategy/3.3/CMAOperators/CMAEqualweightedRecombinator.cs @ 14172

Last change on this file since 14172 was 12009, checked in by ascheibe, 10 years ago

#2212 updated copyright year

File size: 1.8 KB
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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 Equal-weighted Recombinator", "Calculates weighted mean using equal weights.")]
28  [StorableClass]
29  public class CMAEqualweightedRecombinator : CMARecombinator {
30
31    [StorableConstructor]
32    protected CMAEqualweightedRecombinator(bool deserializing) : base(deserializing) { }
33    protected CMAEqualweightedRecombinator(CMAEqualweightedRecombinator original, Cloner cloner) : base(original, cloner) { }
34    public CMAEqualweightedRecombinator() : base() { }
35
36    public override IDeepCloneable Clone(Cloner cloner) {
37      return new CMAEqualweightedRecombinator(this, cloner);
38    }
39
40    protected override double[] GetWeights(int mu) {
41      var weights = new double[mu];
42      for (int i = 0; i < mu; i++)
43        weights[i] = 1.0 / mu;
44      return weights;
45    }
46  }
47}
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