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Timestamp:
11/24/15 17:08:13 (9 years ago)
Author:
ascheibe
Message:

#2520 added guids to storable classes

Location:
branches/PersistenceOverhaul/HeuristicLab.Encodings.RealVectorEncoding/3.3/Crossovers
Files:
12 edited

Legend:

Unmodified
Added
Removed

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.RealVectorEncoding/3.3/Crossovers/AverageCrossover.cs

    r12012 r13368  
    1 #region License Information
     1#region License Information
    22/* HeuristicLab
    33 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    … …  
    3333  /// </remarks>
    3434  [Item("AverageCrossover", "The average crossover (intermediate recombination) produces a new offspring by calculating in each position the average of a number of parents. It is implemented as described by Beyer, H.-G. and Schwefel, H.-P. 2002. Evolution Strategies - A Comprehensive Introduction Natural Computing, 1, pp. 3-52.")]
    35   [StorableClass]
     35  [StorableClass("633EF75E-BD1A-4B89-86FA-EC785B4E2D28")]
    3636  public class AverageCrossover : RealVectorCrossover {
    3737    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.RealVectorEncoding/3.3/Crossovers/BlendAlphaBetaCrossover.cs

    r12012 r13368  
    1 #region License Information
     1#region License Information
    22/* HeuristicLab
    33 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    … …  
    4040  /// </remarks>
    4141  [Item("BlendAlphaBetaCrossover", "The blend alpha beta crossover (BLX-a-b) for real vectors is similar to the blend alpha crossover (BLX-a), but distinguishes between the better and worse of the parents. The interval from which to choose the new offspring can be extended beyond the better parent by specifying a higher alpha value, and beyond the worse parent by specifying a higher beta value. The new offspring is sampled uniformly in the extended range. It is implemented as described in Takahashi, M. and Kita, H. 2001. A crossover operator using independent component analysis for real-coded genetic algorithms Proceedings of the 2001 Congress on Evolutionary Computation, pp. 643-649.")]
    42   [StorableClass]
     42  [StorableClass("087B7AC1-6925-436A-B2D8-757E617DA206")]
    4343  public class BlendAlphaBetaCrossover : RealVectorCrossover, ISingleObjectiveOperator {
    4444    /// <summary>

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.RealVectorEncoding/3.3/Crossovers/BlendAlphaCrossover.cs

    r12012 r13368  
    1 #region License Information
     1#region License Information
    22/* HeuristicLab
    33 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    … …  
    3838  /// </remarks>
    3939  [Item("BlendAlphaCrossover", "The blend alpha crossover (BLX-a) for real vectors creates new offspring by sampling a new value in the range [min_i - d * alpha, max_i + d * alpha) at each position i. Here min_i and max_i are the smaller and larger value of the two parents at position i and d is max_i - min_i. It is implemented as described in Takahashi, M. and Kita, H. 2001. A crossover operator using independent component analysis for real-coded genetic algorithms Proceedings of the 2001 Congress on Evolutionary Computation, pp. 643-649.")]
    40   [StorableClass]
     40  [StorableClass("8506B15A-8091-47BE-B75B-FF454895FB34")]
    4141  public class BlendAlphaCrossover : RealVectorCrossover {
    4242    /// <summary>

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.RealVectorEncoding/3.3/Crossovers/DiscreteCrossover.cs

    r12012 r13368  
    1 #region License Information
     1#region License Information
    22/* HeuristicLab
    33 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    … …  
    3535  /// </remarks>
    3636  [Item("DiscreteCrossover", "Discrete crossover for real vectors: Creates a new offspring by combining the alleles in the parents such that each allele is randomly selected from one parent. It is implemented as described in Beyer, H.-G. and Schwefel, H.-P. 2002. Evolution Strategies - A Comprehensive Introduction Natural Computing, 1, pp. 3-52.")]
    37   [StorableClass]
     37  [StorableClass("23A39268-6A9D-468E-AD99-10599558DE7B")]
    3838  public class DiscreteCrossover : RealVectorCrossover {
    3939    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.RealVectorEncoding/3.3/Crossovers/HeuristicCrossover.cs

    r12012 r13368  
    1 #region License Information
     1#region License Information
    22/* HeuristicLab
    33 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    … …  
    3737  /// </remarks>
    3838  [Item("HeuristicCrossover", "The heuristic crossover produces offspring that extend the better parent in direction from the worse to the better parent. It is implemented as described in Wright, A.H. (1994), Genetic algorithms for real parameter optimization, Foundations of Genetic Algorithms, G.J.E. Rawlins (Ed.), Morgan Kaufmann, San Mateo, CA, 205-218.")]
    39   [StorableClass]
     39  [StorableClass("F1A85C12-A841-48A4-8BB3-D24CA29AC69C")]
    4040  public class HeuristicCrossover : RealVectorCrossover, ISingleObjectiveOperator {
    4141    /// <summary>

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.RealVectorEncoding/3.3/Crossovers/LocalCrossover.cs

    r12012 r13368  
    1 #region License Information
     1#region License Information
    22/* HeuristicLab
    33 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    … …  
    3333  /// </remarks>
    3434  [Item("LocalCrossover", @"The local crossover is similar to the arithmetic all positions crossover, but uses a random alpha for each position x = alpha * p1 + (1-alpha) * p2. It is implemented as described in Dumitrescu, D. et al. (2000), Evolutionary computation, CRC Press, Boca Raton, FL., p. 194.")]
    35   [StorableClass]
     35  [StorableClass("521BC15A-CB2F-47DA-B7BA-D266149B8F5F")]
    3636  public class LocalCrossover : RealVectorCrossover {
    3737    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.RealVectorEncoding/3.3/Crossovers/MultiRealVectorCrossover.cs

    r12012 r13368  
    1 #region License Information
     1#region License Information
    22/* HeuristicLab
    33 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    … …  
    3434namespace HeuristicLab.Encodings.RealVectorEncoding {
    3535  [Item("MultiRealVectorCrossover", "Randomly selects and applies one of its crossovers every time it is called.")]
    36   [StorableClass]
     36  [StorableClass("DEC2B1FD-2F80-4A3E-9E9B-C6214B2761DD")]
    3737  public class MultiRealVectorCrossover : StochasticMultiBranch<IRealVectorCrossover>, IRealVectorCrossover, IStochasticOperator {
    3838    public override bool CanChangeName {

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.RealVectorEncoding/3.3/Crossovers/RandomConvexCrossover.cs

    r12012 r13368  
    1 #region License Information
     1#region License Information
    22/* HeuristicLab
    33 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    … …  
    3333  /// </remarks>
    3434  [Item("RandomConvexCrossover", "The random convex crossover acts like the local crossover, but with just one randomly chosen alpha for all crossed positions. It is implementes as described in Dumitrescu, D. et al. (2000), Evolutionary computation, CRC Press, Boca Raton, FL, pp. 193 - 194.")]
    35   [StorableClass]
     35  [StorableClass("B39B5390-EFEF-478A-8574-A754A2CE26A7")]
    3636  public class RandomConvexCrossover : RealVectorCrossover {
    3737    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.RealVectorEncoding/3.3/Crossovers/SimulatedBinaryCrossover.cs

    r12012 r13368  
    1 #region License Information
     1#region License Information
    22/* HeuristicLab
    33 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    … …  
    3636  /// </remarks>
    3737  [Item("SimulatedBinaryCrossover", "The simulated binary crossover (SBX) is implemented as described in Deb, K. and Agrawal, R. B. 1995. Simulated binary crossover for continuous search space. Complex Systems, 9, pp. 115-148.")]
    38   [StorableClass]
     38  [StorableClass("5FF68A72-B072-4A3D-8546-456B25A90D2D")]
    3939  public class SimulatedBinaryCrossover : RealVectorCrossover {
    4040    /// <summary>

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.RealVectorEncoding/3.3/Crossovers/SinglePointCrossover.cs

    r12012 r13368  
    3535  /// </remarks>
    3636  [Item("SinglePointCrossover", "Breaks both parent chromosomes at a randomly chosen point and assembles a child by taking one part of the first parent and the other part of the second pard. It is implemented as described in Michalewicz, Z. 1999. Genetic Algorithms + Data Structures = Evolution Programs. Third, Revised and Extended Edition, Spring-Verlag Berlin Heidelberg.")]
    37   [StorableClass]
     37  [StorableClass("F130CB4D-94AA-4314-B399-9A5E7582CD0E")]
    3838  public class SinglePointCrossover : RealVectorCrossover {
    3939    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.RealVectorEncoding/3.3/Crossovers/UniformAllPositionsArithmeticCrossover.cs

    r12012 r13368  
    1 #region License Information
     1#region License Information
    22/* HeuristicLab
    33 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    … …  
    3636  /// </remarks>
    3737  [Item("UniformAllPositionsArithmeticCrossover", "The uniform all positions arithmetic crossover constructs an offspring by calculating x = alpha * p1 + (1-alpha) * p2 for every position x in the vector. Note that for alpha = 0.5 it is the same as the AverageCrossover (except that the AverageCrossover is defined for more than 2 parents). It is implemented as described in Michalewicz, Z. 1999. Genetic Algorithms + Data Structures = Evolution Programs. Third, Revised and Extended Edition, Spring-Verlag Berlin Heidelberg.")]
    38   [StorableClass]
     38  [StorableClass("29E697F1-FDA2-4E22-9607-01A17C7A9E70")]
    3939  public class UniformAllPositionsArithmeticCrossover : RealVectorCrossover {
    4040    /// <summary>

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.RealVectorEncoding/3.3/Crossovers/UniformSomePositionsArithmeticCrossover.cs

    r12012 r13368  
    1 #region License Information
     1#region License Information
    22/* HeuristicLab
    33 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    … …  
    3636  /// </remarks>
    3737  [Item("UniformSomePositionsArithmeticCrossover", "The uniform some positions arithmetic crossover (continuous recombination) constructs an offspring by calculating x = alpha * p1 + (1-alpha) * p2 for a position x in the vector with a given probability (otherwise p1 is taken at this position). It is implemented as described in Dumitrescu, D. et al. (2000), Evolutionary computation, CRC Press, Boca Raton, FL, p. 191. Note that Dumitrescu et al. specify the alpha to be 0.5.")]
    38   [StorableClass]
     38  [StorableClass("8F762A73-A651-41EB-B5E6-F045124418B3")]
    3939  public class UniformSomePositionsArithmeticCrossover : RealVectorCrossover {
    4040    /// <summary>
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