source: branches/HeuristicLab.Hive_Milestone2/sources/HeuristicLab.RealVector/3.2/MichalewiczNonUniformOnePositionManipulator.cs @ 6319

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2008 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Text;
using HeuristicLab.Core;
using HeuristicLab.Data;

namespace HeuristicLab.RealVector {
  /// <summary>
  /// Michalewicz, Z. (1992). Genetic Algorithms + Data Structures = Evolution Programs<br/>
  /// Non-uniformly distributed change of one position of a real vector.
  /// </summary>
  public class MichalewiczNonUniformOnePositionManipulator : RealVectorManipulatorBase {
    /// <inheritdoc select="summary"/>
    public override string Description {
      get { return
@"Non-uniformly distributed change of one position of a real vector (Michalewicz 1992)
Initially, the space will be searched uniformly and very locally at later stages. This increases the probability of generating the new number closer to its successor instead of a random number.

Michalewicz, Z. (1992). Genetic Algorithms + Data Structures = Evolution Programs. Springer Verlag.";
      }
    }

    /// <summary>
    /// Initializes a new instance of <see cref="MichalewiczNonUniformOnePositionManipulator"/> with five 
    /// variable infos (<c>Minimum</c>, <c>Maximum</c>, <c>CurrentGeneration</c>, <c>MaximumGenerations</c>
    /// and <c>GenerationsDependency</c>).
    /// </summary>
    public MichalewiczNonUniformOnePositionManipulator()
      : base() {
      AddVariableInfo(new VariableInfo("Minimum", "Minimum of the sampling range for the vector element (included)", typeof(DoubleData), VariableKind.In));
      AddVariableInfo(new VariableInfo("Maximum", "Maximum of the sampling range for the vector element (excluded)", typeof(DoubleData), VariableKind.In));
      AddVariableInfo(new VariableInfo("CurrentGeneration", "Current Generation of the algorithm", typeof(IntData), VariableKind.In));
      AddVariableInfo(new VariableInfo("MaximumGenerations", "Maximum number of Generations", typeof(IntData), VariableKind.In));
      VariableInfo genDepInfo = new VariableInfo("GenerationsDependency", "Specifies the degree of dependency on the number of generations", typeof(IntData), VariableKind.In);
      genDepInfo.Local = true;
      AddVariableInfo(genDepInfo);
      AddVariable(new Variable("GenerationsDependency", new IntData(5)));
    }

    /// <summary>
    /// Performs a non uniformly distributed one position manipulation on the given 
    /// real <paramref name="vector"/>, published by Z. Michalewicz, 1992.
    /// </summary>
    /// <remarks>Calls <see cref="Apply"/>.</remarks>
    /// <param name="scope">The current scope.</param>
    /// <param name="random">The random number generator.</param>
    /// <param name="vector">The real vector to manipulate.</param>
    /// <returns>The manipulated real vector.</returns>
    protected override double[] Manipulate(IScope scope, IRandom random, double[] vector) {
      double min = GetVariableValue<DoubleData>("Minimum", scope, true).Data;
      double max = GetVariableValue<DoubleData>("Maximum", scope, true).Data;
      int currentGeneration = GetVariableValue<IntData>("CurrentGeneration", scope, true).Data;
      int maximumGenerations = GetVariableValue<IntData>("MaximumGenerations", scope, true).Data;
      int generationsDependency = GetVariableValue<IntData>("GenerationsDependency", scope, true).Data;
      return Apply(random, vector, min, max, currentGeneration, maximumGenerations, generationsDependency);
    }

    /// <summary>
    /// Performs a non uniformly distributed one position manipulation on the given 
    /// real <paramref name="vector"/>, published by Z. Michalewicz, 1992.
    /// </summary>
    /// <param name="random">The random number generator.</param>
    /// <param name="vector">The real vector to manipulate.</param>
    /// <param name="min">The minimum value of the sampling range for the vector element (inclusive).</param>
    /// <param name="max">The maximum value of the sampling range for the vector element (exclusive).</param>
    /// <param name="currentGeneration">The current generation of the algorithm.</param>
    /// <param name="maximumGenerations">Maximum number of generations.</param>
    /// <param name="generationsDependency">Specifies the degree of dependency on the number of generations.</param>
    /// <returns>The manipulated real vector.</returns>
    public static double[] Apply(IRandom random, double[] vector, double min, double max, int currentGeneration, int maximumGenerations, int generationsDependency) {
      int length = vector.Length;
      double[] result = new double[length];
      int pos = random.Next(length);

      if (random.NextDouble() < 0.5) {
        vector[pos] = vector[pos] + Delta(random, currentGeneration, max - vector[pos], maximumGenerations, generationsDependency);
      } else {
        vector[pos] = vector[pos] - Delta(random, currentGeneration, vector[pos] - min, maximumGenerations, generationsDependency);
      }
      return vector;
    }

    // returns a value between 0 and y (both included)
    private static double Delta(IRandom random, int currentGeneration, double y, int maximumGenerations, int generationsDependency) {
      return y * (1 - Math.Pow(random.NextDouble(), Math.Pow(1 - currentGeneration / maximumGenerations, generationsDependency)));
    }
  }
}