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Ignore:
Timestamp:
05/04/17 17:19:35 (8 years ago)
Author:
gkronber
Message:

#2520: changed all usages of StorableClass to use StorableType with an auto-generated GUID (did not add StorableType to other type definitions yet)

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

Legend:

Unmodified
Added
Removed

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

    r14185 r14927  
    2323using HeuristicLab.Common;
    2424using HeuristicLab.Core;
    25 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
     25using HeuristicLab.Persistence;
    2626
    2727namespace HeuristicLab.Encodings.RealVectorEncoding {
    … …  
    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  [StorableType("61dc5c88-d9c3-42c9-8232-38c9e5af09fe")]
    3636  public class AverageCrossover : RealVectorCrossover {
    3737    [StorableConstructor]
    … …  
    4343      return new AverageCrossover(this, cloner);
    4444    }
    45    
     45
    4646    /// <summary>
    4747    /// Performs the average crossover (intermediate recombination) on a list of parents.
    … …  
    6666          result[i] = avg / (double)parentsCount;
    6767        }
    68       }
    69       catch (IndexOutOfRangeException) {
     68      } catch (IndexOutOfRangeException) {
    7069        throw new ArgumentException("AverageCrossover: The parents' vectors are of different length.", "parents");
    7170      }

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

    r14185 r14927  
    2626using HeuristicLab.Optimization;
    2727using HeuristicLab.Parameters;
    28 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
     28using HeuristicLab.Persistence;
    2929
    3030namespace HeuristicLab.Encodings.RealVectorEncoding {
    … …  
    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  [StorableType("9bf6736e-2e38-49ee-a154-87d8f0b2cd7c")]
    4343  public class BlendAlphaBetaCrossover : RealVectorCrossover, ISingleObjectiveOperator {
    4444    /// <summary>

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

    r14185 r14927  
    2525using HeuristicLab.Data;
    2626using HeuristicLab.Parameters;
    27 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
     27using HeuristicLab.Persistence;
    2828
    2929namespace HeuristicLab.Encodings.RealVectorEncoding {
    … …  
    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  [StorableType("3a396378-c7ff-4f3b-bea1-4d29d7100bb0")]
    4141  public class BlendAlphaCrossover : RealVectorCrossover {
    4242    /// <summary>

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

    r14185 r14927  
    2323using HeuristicLab.Common;
    2424using HeuristicLab.Core;
    25 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
     25using HeuristicLab.Persistence;
    2626
    2727namespace HeuristicLab.Encodings.RealVectorEncoding {
    … …  
    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  [StorableType("455868ff-d615-409d-8b42-1134934ac6db")]
    3838  public class DiscreteCrossover : RealVectorCrossover {
    3939    [StorableConstructor]
    … …  
    4545      return new DiscreteCrossover(this, cloner);
    4646    }
    47    
     47
    4848    /// <summary>
    4949    /// Performs a discrete crossover operation on multiple parents.

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

    r14185 r14927  
    2626using HeuristicLab.Optimization;
    2727using HeuristicLab.Parameters;
    28 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
     28using HeuristicLab.Persistence;
    2929
    3030namespace HeuristicLab.Encodings.RealVectorEncoding {
    … …  
    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  [StorableType("2fbe64ff-f4fe-4668-99a5-a0af853f69e7")]
    4040  public class HeuristicCrossover : RealVectorCrossover, ISingleObjectiveOperator {
    4141    /// <summary>

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

    r14185 r14927  
    2323using HeuristicLab.Common;
    2424using HeuristicLab.Core;
    25 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
     25using HeuristicLab.Persistence;
    2626
    2727namespace HeuristicLab.Encodings.RealVectorEncoding {
    … …  
    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  [StorableType("263eb3c9-5288-47d4-814f-ca9de5356887")]
    3636  public class LocalCrossover : RealVectorCrossover {
    3737    [StorableConstructor]
    … …  
    4343      return new LocalCrossover(this, cloner);
    4444    }
    45    
     45
    4646    /// <summary>
    4747    /// Performs a local crossover on the two given parent vectors.

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

    r14185 r14927  
    2929using HeuristicLab.Optimization;
    3030using HeuristicLab.Parameters;
    31 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
     31using HeuristicLab.Persistence;
    3232using HeuristicLab.PluginInfrastructure;
    3333
    3434namespace HeuristicLab.Encodings.RealVectorEncoding {
    3535  [Item("MultiRealVectorCrossover", "Randomly selects and applies one of its crossovers every time it is called.")]
    36   [StorableClass]
     36  [StorableType("f1b6b83e-6980-46d5-b0ef-539b0f04a51e")]
    3737  public class MultiRealVectorCrossover : StochasticMultiBranch<IRealVectorCrossover>, IRealVectorCrossover, IStochasticOperator {
    3838    public override bool CanChangeName {

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

    r14185 r14927  
    2323using HeuristicLab.Common;
    2424using HeuristicLab.Core;
    25 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
     25using HeuristicLab.Persistence;
    2626
    2727namespace HeuristicLab.Encodings.RealVectorEncoding {
    … …  
    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  [StorableType("6742955d-1e35-4210-b74b-c1b31f0c02a0")]
    3636  public class RandomConvexCrossover : RealVectorCrossover {
    3737    [StorableConstructor]
    … …  
    4343      return new RandomConvexCrossover(this, cloner);
    4444    }
    45    
     45
    4646    /// <summary>
    4747    /// Performs a random convex crossover on the two given parents.

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

    r14185 r14927  
    2525using HeuristicLab.Data;
    2626using HeuristicLab.Parameters;
    27 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
     27using HeuristicLab.Persistence;
    2828
    2929namespace HeuristicLab.Encodings.RealVectorEncoding {
    … …  
    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  [StorableType("1ed2ad31-7d33-481a-95bd-0cdbf7cd22c8")]
    3939  public class SimulatedBinaryCrossover : RealVectorCrossover {
    4040    /// <summary>

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

    r14185 r14927  
    2323using HeuristicLab.Common;
    2424using HeuristicLab.Core;
    25 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
     25using HeuristicLab.Persistence;
    2626
    2727namespace HeuristicLab.Encodings.RealVectorEncoding {
    … …  
    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  [StorableType("92055f8b-3b1e-4e61-bcdf-212964dbd3fd")]
    3838  public class SinglePointCrossover : RealVectorCrossover {
    3939    [StorableConstructor]
    … …  
    4545      return new SinglePointCrossover(this, cloner);
    4646    }
    47    
     47
    4848    /// <summary>
    4949    /// Performs the single point crossover for real vectors. The implementation is similar to the single point crossover for binary vectors.

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

    r14185 r14927  
    2525using HeuristicLab.Data;
    2626using HeuristicLab.Parameters;
    27 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
     27using HeuristicLab.Persistence;
    2828
    2929namespace HeuristicLab.Encodings.RealVectorEncoding {
    … …  
    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  [StorableType("51c7c3c0-2d86-4523-ab3d-705e74a43486")]
    3939  public class UniformAllPositionsArithmeticCrossover : RealVectorCrossover {
    4040    /// <summary>

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

    r14185 r14927  
    2525using HeuristicLab.Data;
    2626using HeuristicLab.Parameters;
    27 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
     27using HeuristicLab.Persistence;
    2828
    2929namespace HeuristicLab.Encodings.RealVectorEncoding {
    … …  
    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  [StorableType("76030d4b-a514-435d-abc2-0feb530a2b74")]
    3939  public class UniformSomePositionsArithmeticCrossover : RealVectorCrossover {
    4040    /// <summary>
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