#region License Information /* HeuristicLab * Copyright (C) 2002-2015 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.Default.CompositeSerializers.Storable; namespace HeuristicLab.Encodings.IntegerVectorEncoding { /// /// Heuristic crossover for integer vectors: Calculates the vector from the worse to the better parent and adds that to the better parent weighted with a factor in the interval [0;1). /// The result is then rounded to the next feasible integer. /// The idea is that going further in direction from the worse to the better leads to even better solutions (naturally this depends on the fitness landscape). /// [Item("RoundedHeuristicCrossover", "The heuristic crossover produces offspring that extend the better parent in direction from the worse to the better parent.")] [StorableType("D34DD2C1-FE8D-47B9-8E1E-D9962B9152DB")] public class RoundedHeuristicCrossover : BoundedIntegerVectorCrossover, 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"]; } } [StorableConstructor] protected RoundedHeuristicCrossover(bool deserializing) : base(deserializing) { } protected RoundedHeuristicCrossover(RoundedHeuristicCrossover original, Cloner cloner) : base(original, cloner) { } /// /// Initializes a new instance of with two variable infos /// (Maximization and Quality). /// public RoundedHeuristicCrossover() : 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.")); } public override IDeepCloneable Clone(Cloner cloner) { return new RoundedHeuristicCrossover(this, cloner); } /// /// Perfomrs a heuristic crossover on the two given parents. /// /// Thrown when two parents are not of the same length. /// The random number generator. /// The first parent for the crossover operation. /// The second parent for the crossover operation. /// The bounds and step size for each dimension (will be cycled in case there are less rows than elements in the parent vectors). /// The newly created integer vector, resulting from the heuristic crossover. public static IntegerVector Apply(IRandom random, IntegerVector betterParent, IntegerVector worseParent, IntMatrix bounds) { if (betterParent.Length != worseParent.Length) throw new ArgumentException("HeuristicCrossover: the two parents are not of the same length"); int length = betterParent.Length; var result = new IntegerVector(length); double factor = random.NextDouble(); int min, max, step = 1; for (int i = 0; i < length; i++) { min = bounds[i % bounds.Rows, 0]; max = bounds[i % bounds.Rows, 1]; if (bounds.Columns > 2) step = bounds[i % bounds.Rows, 2]; max = FloorFeasible(min, max, step, max - 1); result[i] = RoundFeasible(min, max, step, betterParent[i] + factor * (betterParent[i] - worseParent[i])); } return result; } /// /// Performs a heuristic crossover operation for two given parent integer vectors. /// /// 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. /// ///
/// A random number generator. /// An array containing the two real vectors that should be crossed. /// /// The bounds and step size for each dimension (will be cycled in case there are less rows than elements in the parent vectors). /// The newly created integer vector, resulting from the crossover operation. protected override IntegerVector CrossBounded(IRandom random, ItemArray parents, IntMatrix bounds) { if (parents.Length != 2) throw new ArgumentException("RoundedHeuristicCrossover: The number of parents is not equal to 2"); if (MaximizationParameter.ActualValue == null) throw new InvalidOperationException("RoundedHeuristicCrossover: Parameter " + MaximizationParameter.ActualName + " could not be found."); if (QualityParameter.ActualValue == null || QualityParameter.ActualValue.Length != parents.Length) throw new InvalidOperationException("RoundedHeuristicCrossover: Parameter " + QualityParameter.ActualName + " could not be found, or not in the same quantity as there are parents."); 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], bounds); else return Apply(random, parents[1], parents[0], bounds); } } }