[9129] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[17181] | 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[9129] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using HeuristicLab.Common;
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| 23 | using HeuristicLab.Core;
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[17097] | 24 | using HEAL.Attic;
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[9129] | 25 |
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| 26 | namespace HeuristicLab.Algorithms.CMAEvolutionStrategy {
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[9709] | 27 | [Item("CMA Linear-weighted Recombinator", "Calculates weighted mean using linear decreasing weights.")]
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[17097] | 28 | [StorableType("628D27E7-AD3F-418B-A44D-E5338017CA69")]
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[9129] | 29 | public class CMALinearweightedRecombinator : CMARecombinator {
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| 30 |
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| 31 | [StorableConstructor]
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[17097] | 32 | protected CMALinearweightedRecombinator(StorableConstructorFlag _) : base(_) { }
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[9129] | 33 | protected CMALinearweightedRecombinator(CMALinearweightedRecombinator original, Cloner cloner) : base(original, cloner) { }
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| 34 | public CMALinearweightedRecombinator() : base() { }
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| 35 |
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| 36 | public override IDeepCloneable Clone(Cloner cloner) {
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| 37 | return new CMALinearweightedRecombinator(this, cloner);
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| 38 | }
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| 39 |
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[9297] | 40 | protected override double[] GetWeights(int mu) {
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| 41 | var weights = new double[mu];
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[9129] | 42 | var sum = (mu + 1) * mu / 2.0; // sum of arithmetic progression mu..1
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| 43 | for (int i = 0; i < mu; i++)
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| 44 | weights[i] = (mu - i) / sum;
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| 45 | return weights;
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| 46 | }
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| 47 | }
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| 48 | } |
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