1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022019 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.Optimization;


26  using HeuristicLab.Parameters;


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


28 


29  namespace HeuristicLab.Encodings.RealVectorEncoding {


30  [Item("StochasticPolynomialMultiMoveGenerator", "Generates polynomial moves from a given real vector.")]


31  [StorableClass]


32  public class StochasticPolynomialMultiMoveGenerator : AdditiveMoveGenerator, IMultiMoveGenerator {


33  /// <summary>


34  /// The maximum manipulation parameter specifies the range of the manipulation. The value specified here is the highest value the mutation will ever add to the current value.


35  /// </summary>


36  public ValueLookupParameter<DoubleValue> MaximumManipulationParameter {


37  get { return (ValueLookupParameter<DoubleValue>)Parameters["MaximumManipulation"]; }


38  }


39  /// <summary>


40  /// The contiguity parameter specifies the shape of the probability density function that controls the mutation. Setting it to 0 is similar to a uniform distribution over the entire manipulation range (specified by <see cref="MaximumManipulationParameter"/>.


41  /// A higher value will shape the density function such that values closer to 0 (little manipulation) are more likely than values closer to 1 or 1 (maximum manipulation).


42  /// </summary>


43  public IValueLookupParameter<DoubleValue> ContiguityParameter {


44  get { return (IValueLookupParameter<DoubleValue>)Parameters["Contiguity"]; }


45  }


46  public IValueLookupParameter<IntValue> SampleSizeParameter {


47  get { return (IValueLookupParameter<IntValue>)Parameters["SampleSize"]; }


48  }


49 


50  [StorableConstructor]


51  protected StochasticPolynomialMultiMoveGenerator(bool deserializing) : base(deserializing) { }


52  protected StochasticPolynomialMultiMoveGenerator(StochasticPolynomialMultiMoveGenerator original, Cloner cloner) : base(original, cloner) { }


53  public StochasticPolynomialMultiMoveGenerator()


54  : base() {


55  Parameters.Add(new ValueLookupParameter<DoubleValue>("Contiguity", "Specifies whether the manipulation should produce far stretching (small value) or close (large value) manipulations with higher probability. Valid values must be greater or equal to 0.", new DoubleValue(2)));


56  Parameters.Add(new ValueLookupParameter<IntValue>("SampleSize", "The number of moves that should be generated."));


57  Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumManipulation", "Specifies the maximum value that should be added or subtracted by the manipulation. If this value is set to 0 no mutation will be performed.", new DoubleValue(1)));


58  }


59 


60  public override IDeepCloneable Clone(Cloner cloner) {


61  return new StochasticPolynomialMultiMoveGenerator(this, cloner);


62  }


63 


64  public static AdditiveMove[] Apply(IRandom random, RealVector vector, double contiguity, int sampleSize, double maxManipulation, DoubleMatrix bounds) {


65  AdditiveMove[] moves = new AdditiveMove[sampleSize];


66  for (int i = 0; i < sampleSize; i++) {


67  int index = random.Next(vector.Length);


68  double strength = 0, min = bounds[index % bounds.Rows, 0], max = bounds[index % bounds.Rows, 1];


69  do {


70  strength = PolynomialOnePositionManipulator.Apply(random, contiguity) * maxManipulation;


71  } while (vector[index] + strength < min  vector[index] + strength > max);


72  moves[i] = new AdditiveMove(index, strength);


73  }


74  return moves;


75  }


76 


77  protected override AdditiveMove[] GenerateMoves(IRandom random, RealVector realVector, DoubleMatrix bounds) {


78  return Apply(random, realVector, ContiguityParameter.ActualValue.Value, SampleSizeParameter.ActualValue.Value, MaximumManipulationParameter.ActualValue.Value, bounds);


79  }


80  }


81  }

