1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)


4  *


5  * This file is part of HeuristicLab.


6  *


7  * HeuristicLab is free software: you can redistribute it and/or modify


8  * it under the terms of the GNU General Public License as published by


9  * the Free Software Foundation, either version 3 of the License, or


10  * (at your option) any later version.


11  *


12  * HeuristicLab is distributed in the hope that it will be useful,


13  * but WITHOUT ANY WARRANTY; without even the implied warranty of


14  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the


15  * GNU General Public License for more details.


16  *


17  * You should have received a copy of the GNU General Public License


18  * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.


19  */


20  #endregion


21 


22  using HeuristicLab.Common;


23  using HeuristicLab.Core;


24  using HeuristicLab.Data;


25  using HeuristicLab.Encodings.RealVectorEncoding;


26  using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;


27 


28  namespace HeuristicLab.Problems.TestFunctions {


29  /// <summary>


30  /// The Sum Squares function is defined as sum(i * x_i * x_i) for i = 1..n


31  /// </summary>


32  [Item("SumSquaresEvaluator", "Evaluates the sum squares function on a given point. The optimum of this function is 0 at the origin. The Sum Squares function is defined as sum(i * x_i * x_i) for i = 1..n.")]


33  [StorableClass]


34  public class SumSquaresEvaluator : SingleObjectiveTestFunctionProblemEvaluator {


35  public override string FunctionName { get { return "SumSquares"; } }


36  /// <summary>


37  /// Returns false as the Sum Squares function is a minimization problem.


38  /// </summary>


39  public override bool Maximization {


40  get { return false; }


41  }


42  /// <summary>


43  /// Gets the optimum function value (0).


44  /// </summary>


45  public override double BestKnownQuality {


46  get { return 0; }


47  }


48  /// <summary>


49  /// Gets the lower and upper bound of the function.


50  /// </summary>


51  public override DoubleMatrix Bounds {


52  get { return new DoubleMatrix(new double[,] { { 10, 10 } }); }


53  }


54  /// <summary>


55  /// Gets the minimum problem size (1).


56  /// </summary>


57  public override int MinimumProblemSize {


58  get { return 1; }


59  }


60  /// <summary>


61  /// Gets the (theoretical) maximum problem size (2^31  1).


62  /// </summary>


63  public override int MaximumProblemSize {


64  get { return int.MaxValue; }


65  }


66 


67  [StorableConstructor]


68  protected SumSquaresEvaluator(bool deserializing) : base(deserializing) { }


69  protected SumSquaresEvaluator(SumSquaresEvaluator original, Cloner cloner) : base(original, cloner) { }


70  public SumSquaresEvaluator() : base() { }


71 


72  public override IDeepCloneable Clone(Cloner cloner) {


73  return new SumSquaresEvaluator(this, cloner);


74  }


75 


76  public override RealVector GetBestKnownSolution(int dimension) {


77  return new RealVector(dimension);


78  }


79 


80  /// <summary>


81  /// Evaluates the test function for a specific <paramref name="point"/>.


82  /// </summary>


83  /// <param name="point">Ndimensional point for which the test function should be evaluated.</param>


84  /// <returns>The result value of the Sum Squares function at the given point.</returns>


85  public static double Apply(RealVector point) {


86  double result = 0;


87  for (int i = 0; i < point.Length; i++) {


88  result += (i + 1) * point[i] * point[i];


89  }


90  return result;


91  }


92 


93  /// <summary>


94  /// Evaluates the test function for a specific <paramref name="point"/>.


95  /// </summary>


96  /// <remarks>Calls <see cref="Apply"/>.</remarks>


97  /// <param name="point">Ndimensional point for which the test function should be evaluated.</param>


98  /// <returns>The result value of the Sum Squares function at the given point.</returns>


99  public override double Evaluate(RealVector point) {


100  return Apply(point);


101  }


102  }


103  }

