#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);
}
}
}