#region License Information /* HeuristicLab * Copyright (C) 2002-2010 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.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.RealVectorEncoding; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.TestFunctions { /// /// The Rosenbrock function features a flat valley in which the global optimum is located. /// It is implemented as generalized Rosenbrock function as for example given in Shang, Y.-W. and Qiu, Y.-H. 2006. A Note on the Extended Rosenbrock Function. Evolutionary Computation 14, pp. 119-126, MIT Press. /// [Item("RosenbrockEvaluator", @"The Rosenbrock function features a flat valley in which the global optimum is located. For 2 and 3 dimensions the single minimum of this function is 0 at (1,1,...,1), for 4 to 30 dimensions there is an additional local minimum close to (-1,1,...,1). It is unknown how many local minima there are for dimensions greater than 30. It is implemented as generalized Rosenbrock function for which the 2 dimensional function is a special case, as for example given in Shang, Y.-W. and Qiu, Y.-H. 2006. A Note on the Extended Rosenbrock Function. Evolutionary Computation 14, pp. 119-126, MIT Press.")] [StorableClass] public class RosenbrockEvaluator : SingleObjectiveTestFunctionProblemEvaluator { /// /// Returns false as the Rosenbrock function is a minimization problem. /// public override bool Maximization { get { return false; } } /// /// Gets the optimum function value (0). /// public override double BestKnownQuality { get { return 0; } } /// /// Gets the lower and upper bound of the function. /// public override DoubleMatrix Bounds { get { return new DoubleMatrix(new double[,] { { -2.048, 2.048 } }); } } /// /// Gets the minimum problem size (2). /// public override int MinimumProblemSize { get { return 2; } } /// /// Gets the (theoretical) maximum problem size (2^31 - 1). /// public override int MaximumProblemSize { get { return int.MaxValue; } } public override RealVector GetBestKnownSolution(int dimension) { if (dimension < 2) throw new ArgumentException(Name + ": This function is not defined for 1 dimension."); RealVector result = new RealVector(dimension); for (int i = 0; i < dimension; i++) result[i] = 1; return result; } /// /// Evaluates the test function for a specific . /// /// N-dimensional point for which the test function should be evaluated. /// The result value of the Rosenbrock function at the given point. public static double Apply(RealVector point) { double result = 0; for (int i = 0; i < point.Length - 1; i++) { result += 100 * (point[i] * point[i] - point[i + 1]) * (point[i] * point[i] - point[i + 1]); result += (point[i] - 1) * (point[i] - 1); } return result; } /// /// Evaluates the test function for a specific . /// /// Calls . /// N-dimensional point for which the test function should be evaluated. /// The result value of the Rosenbrock function at the given point. protected override double EvaluateFunction(RealVector point) { return Apply(point); } } }