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Ignore:
Timestamp:
03/03/17 11:41:43 (8 years ago)
Author:
gkronber
Message:

#2520

  • renamed StorableClass -> StorableType
  • changed persistence to use GUIDs instead of type names
Location:
branches/PersistenceOverhaul/HeuristicLab.Encodings.PermutationEncoding/3.3/Crossovers
Files:
12 edited

Legend:

Unmodified
Added
Removed

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.PermutationEncoding/3.3/Crossovers/CosaCrossover.cs

    r13368 r14711  
    3636  /// </remarks>
    3737  [Item("CosaCrossover", "An operator which performs the crossover described in the COSA optimization method. It is implemented as described in Wendt, O. 1994. COSA: COoperative Simulated Annealing - Integration von Genetischen Algorithmen und Simulated Annealing am Beispiel der Tourenplanung. Dissertation Thesis. IWI Frankfurt.")]
    38   [StorableClass("BE89CCF0-FABE-470A-B3BA-B59A217CDAE4")]
     38  [StorableType("BE89CCF0-FABE-470A-B3BA-B59A217CDAE4")]
    3939  public class CosaCrossover : PermutationCrossover {
    4040    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.PermutationEncoding/3.3/Crossovers/CyclicCrossover.cs

    r13368 r14711  
    3333  /// </remarks>
    3434  [Item("CyclicCrossover", "An operator which performs the cyclic crossover on two permutations. It is implemented as described in Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg.")]
    35   [StorableClass("8B5253AD-59D8-4B36-B9A0-8D0EDB406807")]
     35  [StorableType("8B5253AD-59D8-4B36-B9A0-8D0EDB406807")]
    3636  public class CyclicCrossover : PermutationCrossover {
    3737    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.PermutationEncoding/3.3/Crossovers/CyclicCrossover2.cs

    r13368 r14711  
    3434  /// </remarks>
    3535  [Item("CyclicCrossover2", "An operator which performs the cyclic crossover on two permutations. It is implemented as described in Affenzeller, M. et al. 2009. Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. CRC Press. p. 136.")]
    36   [StorableClass("2C7684AA-EA65-4CDB-B926-450D381CE735")]
     36  [StorableType("2C7684AA-EA65-4CDB-B926-450D381CE735")]
    3737  public class CyclicCrossover2 : PermutationCrossover {
    3838    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.PermutationEncoding/3.3/Crossovers/EdgeRecombinationCrossover.cs

    r13368 r14711  
    3636  /// </remarks>
    3737  [Item("EdgeRecombinationCrossover", "An operator which performs the edge recombination crossover on two permutations. It is implemented as described in Whitley et.al. 1991, The Traveling Salesman and Sequence Scheduling, in Davis, L. (Ed.), Handbook of Genetic Algorithms, New York, pp. 350-372.")]
    38   [StorableClass("507E1E1C-6A26-4222-ABEB-9B3B16A0FD31")]
     38  [StorableType("507E1E1C-6A26-4222-ABEB-9B3B16A0FD31")]
    3939  public class EdgeRecombinationCrossover : PermutationCrossover {
    4040    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.PermutationEncoding/3.3/Crossovers/MaximalPreservativeCrossover.cs

    r13368 r14711  
    3636  /// </remarks>
    3737  [Item("MaximalPreservativeCrossover", "An operator which performs the maximal preservative crossover on two permutations. It is implemented as described in Mï¿œhlenbein, H. 1991. Evolution in time and space - the parallel genetic algorithm. FOUNDATIONS OF GENETIC ALGORITHMS, pp. 316-337. Morgan Kaufmann.")]
    38   [StorableClass("5F971E2A-6209-42F4-856D-8ED7C47345EC")]
     38  [StorableType("5F971E2A-6209-42F4-856D-8ED7C47345EC")]
    3939  public class MaximalPreservativeCrossover : PermutationCrossover {
    4040    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.PermutationEncoding/3.3/Crossovers/MultiPermutationCrossover.cs

    r13368 r14711  
    3333namespace HeuristicLab.Encodings.PermutationEncoding {
    3434  [Item("MultiPermutationCrossover", "Randomly selects and applies one of its crossovers every time it is called.")]
    35   [StorableClass("F350D445-AB72-4B38-91A1-961A9D24141A")]
     35  [StorableType("F350D445-AB72-4B38-91A1-961A9D24141A")]
    3636  public class MultiPermutationCrossover : StochasticMultiBranch<IPermutationCrossover>, IPermutationCrossover, IStochasticOperator {
    3737    public override bool CanChangeName {

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.PermutationEncoding/3.3/Crossovers/OrderBasedCrossover.cs

    r13368 r14711  
    3636  /// </remarks>
    3737  [Item("OrderBasedCrossover", "An operator which performs an order based crossover of two permutations. It is implemented as described in Syswerda, G. (1991). Schedule Optimization Using Genetic Algorithms. In Davis, L. (Ed.) Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, pp. 332-349.")]
    38   [StorableClass("A1E0EEE1-2DD6-49D6-BA9E-181AF690EE05")]
     38  [StorableType("A1E0EEE1-2DD6-49D6-BA9E-181AF690EE05")]
    3939  public class OrderBasedCrossover : PermutationCrossover {
    4040    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.PermutationEncoding/3.3/Crossovers/OrderCrossover.cs

    r13368 r14711  
    3636  /// </remarks>
    3737  [Item("OrderCrossover", "An operator which performs an order crossover of two permutations. It is implemented as described in Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg.")]
    38   [StorableClass("4DE9904A-8F55-477B-AA50-742EADC289BF")]
     38  [StorableType("4DE9904A-8F55-477B-AA50-742EADC289BF")]
    3939  public class OrderCrossover : PermutationCrossover {
    4040    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.PermutationEncoding/3.3/Crossovers/OrderCrossover2.cs

    r13368 r14711  
    3535  /// </remarks>
    3636  [Item("OrderCrossover2", "An operator which performs an order crossover of two permutations. It is implemented as described in Affenzeller, M. et al. 2009. Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. CRC Press. p. 135.")]
    37   [StorableClass("E532BE44-AD31-447A-B831-1BF8C7DD820A")]
     37  [StorableType("E532BE44-AD31-447A-B831-1BF8C7DD820A")]
    3838  public class OrderCrossover2 : PermutationCrossover {
    3939    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.PermutationEncoding/3.3/Crossovers/PartiallyMatchedCrossover.cs

    r13368 r14711  
    3535  /// </remarks>
    3636  [Item("PartiallyMatchedCrossover", "An operator which performs the partially matched crossover on two permutations. It is implemented as described in Fogel, D.B. 1988. An Evolutionary Approach to the Traveling Salesman Problem. Biological Cybernetics, 60, pp. 139-144, Springer-Verlag.")]
    37   [StorableClass("3FDE28EB-89D3-414F-8149-30D3AABA74D3")]
     37  [StorableType("3FDE28EB-89D3-414F-8149-30D3AABA74D3")]
    3838  public class PartiallyMatchedCrossover : PermutationCrossover {
    3939    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.PermutationEncoding/3.3/Crossovers/PositionBasedCrossover.cs

    r13368 r14711  
    3333  /// </remarks>
    3434  [Item("PositionBasedCrossover", "An operator which performs the position based crossover on two permutations. It is implemented as described in Syswerda, G. (1991). Schedule Optimization Using Genetic Algorithms. In Davis, L. (Ed.) Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, pp 332-349.")]
    35   [StorableClass("9DD9F0BF-8F31-49F6-B12D-14B8E9DF9919")]
     35  [StorableType("9DD9F0BF-8F31-49F6-B12D-14B8E9DF9919")]
    3636  public class PositionBasedCrossover : PermutationCrossover {
    3737    [StorableConstructor]

  • branches/PersistenceOverhaul/HeuristicLab.Encodings.PermutationEncoding/3.3/Crossovers/UniformLikeCrossover.cs

    r13368 r14711  
    2828namespace HeuristicLab.Encodings.PermutationEncoding {
    2929  [Item("UniformLikeCrossover", "The ULX crossover tries to maintain the position in the permutation. It randomly chooses from left to right one of its parents' alleles at each position. Missing entries are then filled randomly later. It is described in Tate, D. M. and Smith, A. E. 1995. A genetic approach to the quadratic assignment problem. Computers & Operations Research, vol. 22, pp. 73-83.")]
    30   [StorableClass("B4A26500-F8D1-410A-BBE3-999CB472DF30")]
     30  [StorableType("B4A26500-F8D1-410A-BBE3-999CB472DF30")]
    3131  public sealed class UniformLikeCrossover : PermutationCrossover {
    3232    [StorableConstructor]
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