#region License Information /* HeuristicLab * Copyright (C) 2002-2016 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 * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Optimization; using HeuristicLab.Parameters; using HeuristicLab.Persistence; namespace HeuristicLab.Encodings.RealVectorEncoding { /// /// Blend alpha-beta crossover for real vectors (BLX-a-b). Creates a new offspring by selecting a /// random value from the interval between the two alleles of the parent solutions. /// The interval is increased in both directions as follows: Into the direction of the 'better' /// solution by the factor alpha, into the direction of the 'worse' solution by the factor beta. /// /// /// It is implemented as described in Takahashi, M. and Kita, H. 2001. A crossover operator using independent component analysis for real-coded genetic algorithms Proceedings of the 2001 Congress on Evolutionary Computation, pp. 643-649.
/// The default value for alpha is 0.75, the default value for beta is 0.25. ///
[Item("BlendAlphaBetaCrossover", "The blend alpha beta crossover (BLX-a-b) for real vectors is similar to the blend alpha crossover (BLX-a), but distinguishes between the better and worse of the parents. The interval from which to choose the new offspring can be extended beyond the better parent by specifying a higher alpha value, and beyond the worse parent by specifying a higher beta value. The new offspring is sampled uniformly in the extended range. It is implemented as described in Takahashi, M. and Kita, H. 2001. A crossover operator using independent component analysis for real-coded genetic algorithms Proceedings of the 2001 Congress on Evolutionary Computation, pp. 643-649.")] [StorableType("9bf6736e-2e38-49ee-a154-87d8f0b2cd7c")] public class BlendAlphaBetaCrossover : RealVectorCrossover, ISingleObjectiveOperator { /// /// Whether the problem is a maximization or minimization problem. /// public ValueLookupParameter MaximizationParameter { get { return (ValueLookupParameter)Parameters["Maximization"]; } } /// /// The quality of the parents. /// public ScopeTreeLookupParameter QualityParameter { get { return (ScopeTreeLookupParameter)Parameters["Quality"]; } } /// /// The Alpha parameter controls the extension of the range beyond the better parent. The value must be >= 0 and does not depend on Beta. /// public ValueLookupParameter AlphaParameter { get { return (ValueLookupParameter)Parameters["Alpha"]; } } /// /// The Beta parameter controls the extension of the range beyond the worse parent. The value must be >= 0 and does not depend on Alpha. /// public ValueLookupParameter BetaParameter { get { return (ValueLookupParameter)Parameters["Beta"]; } } [StorableConstructor] protected BlendAlphaBetaCrossover(StorableConstructorFlag deserializing) : base(deserializing) { } protected BlendAlphaBetaCrossover(BlendAlphaBetaCrossover original, Cloner cloner) : base(original, cloner) { } /// /// Initializes a new instance of with four additional parameters /// (Maximization, Quality, Alpha and Beta). /// public BlendAlphaBetaCrossover() : base() { Parameters.Add(new ValueLookupParameter("Maximization", "Whether the problem is a maximization problem or not.")); Parameters.Add(new ScopeTreeLookupParameter("Quality", "The quality values of the parents.")); Parameters.Add(new ValueLookupParameter("Alpha", "The Alpha parameter controls the extension of the range beyond the better parent. The value must be >= 0 and does not depend on Beta.", new DoubleValue(0.75))); Parameters.Add(new ValueLookupParameter("Beta", "The Beta parameter controls the extension of the range beyond the worse parent. The value must be >= 0 and does not depend on Alpha.", new DoubleValue(0.25))); } public override IDeepCloneable Clone(Cloner cloner) { return new BlendAlphaBetaCrossover(this, cloner); } /// /// Performs the blend alpha beta crossover (BLX-a-b) on two parent vectors. /// /// /// Thrown when either:
/// /// The length of and is not equal. /// The parameter is smaller than 0. /// The parameter is smaller than 0. /// ///
/// The random number generator to use. /// The better of the two parents with regard to their fitness. /// The worse of the two parents with regard to their fitness. /// The parameter alpha. /// The parameter beta. /// The real vector that results from the crossover. public static RealVector Apply(IRandom random, RealVector betterParent, RealVector worseParent, DoubleValue alpha, DoubleValue beta) { if (betterParent.Length != worseParent.Length) throw new ArgumentException("BlendAlphaBetaCrossover: The parents' vectors are of different length.", "betterParent"); if (alpha.Value < 0) throw new ArgumentException("BlendAlphaBetaCrossover: Parameter alpha must be greater or equal to 0.", "alpha"); if (beta.Value < 0) throw new ArgumentException("BlendAlphaBetaCrossover: Parameter beta must be greater or equal to 0.", "beta"); int length = betterParent.Length; double min, max, d; RealVector result = new RealVector(length); for (int i = 0; i < length; i++) { d = Math.Abs(betterParent[i] - worseParent[i]); if (betterParent[i] <= worseParent[i]) { min = betterParent[i] - d * alpha.Value; max = worseParent[i] + d * beta.Value; } else { min = worseParent[i] - d * beta.Value; max = betterParent[i] + d * alpha.Value; } result[i] = min + random.NextDouble() * (max - min); } return result; } /// /// Checks if the number of parents is equal to 2, if all parameters are available and forwards the call to . /// /// Thrown when the number of parents is not equal to 2. /// /// Thrown when either:
/// /// Maximization parameter could not be found. /// Quality parameter could not be found or the number of quality values is not equal to the number of parents. /// Alpha parameter could not be found. /// Beta parameter could not be found. /// ///
/// The random number generator to use. /// The collection of parents (must be of size 2). /// The real vector that results from the crossover. protected override RealVector Cross(IRandom random, ItemArray parents) { if (parents.Length != 2) throw new ArgumentException("BlendAlphaBetaCrossover: Number of parents is not equal to 2.", "parents"); if (MaximizationParameter.ActualValue == null) throw new InvalidOperationException("BlendAlphaBetaCrossover: Parameter " + MaximizationParameter.ActualName + " could not be found."); if (QualityParameter.ActualValue == null || QualityParameter.ActualValue.Length != parents.Length) throw new InvalidOperationException("BlendAlphaBetaCrossover: Parameter " + QualityParameter.ActualName + " could not be found, or not in the same quantity as there are parents."); if (AlphaParameter.ActualValue == null || BetaParameter.ActualValue == null) throw new InvalidOperationException("BlendAlphaBetaCrossover: Parameter " + AlphaParameter.ActualName + " or paramter " + BetaParameter.ActualName + " could not be found."); ItemArray qualities = QualityParameter.ActualValue; bool maximization = MaximizationParameter.ActualValue.Value; if (maximization && qualities[0].Value >= qualities[1].Value || !maximization && qualities[0].Value <= qualities[1].Value) return Apply(random, parents[0], parents[1], AlphaParameter.ActualValue, BetaParameter.ActualValue); else { return Apply(random, parents[1], parents[0], AlphaParameter.ActualValue, BetaParameter.ActualValue); } } } }