Changeset 3017 for trunk/sources/HeuristicLab.Encodings.RealVector/3.3/Crossovers

Timestamp:

03/14/10 01:06:17 (15 years ago)

Author:

epitzer

Message:

Merge StorableClassType.Empty into StorableClassType.MarkedOnly and make it the default if not specified (#548)

Location:

trunk/sources/HeuristicLab.Encodings.RealVector/3.3/Crossovers

Files:

• AverageCrossover.cs (modified) (1 diff)
• BlendAlphaBetaCrossover.cs (modified) (1 diff)
• BlendAlphaCrossover.cs (modified) (1 diff)
• DiscreteCrossover.cs (modified) (1 diff)
• HeuristicCrossover.cs (modified) (1 diff)
• LocalCrossover.cs (modified) (1 diff)
• RandomConvexCrossover.cs (modified) (1 diff)
• SimulatedBinaryCrossover.cs (modified) (1 diff)
• SinglePointCrossover.cs (modified) (1 diff)
• UniformAllPositionsArithmeticCrossover.cs (modified) (1 diff)
• UniformSomePositionsArithmeticCrossover.cs (modified) (1 diff)

trunk/sources/HeuristicLab.Encodings.RealVector/3.3/Crossovers/AverageCrossover.cs

@@ -33 +33 @@
   /// </remarks>
   [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.")]
-  [StorableClass(StorableClassType.Empty)]
+  [StorableClass]
   public class AverageCrossover : RealVectorCrossover {
     /// <summary>

trunk/sources/HeuristicLab.Encodings.RealVector/3.3/Crossovers/BlendAlphaBetaCrossover.cs

@@ -38 +38 @@
   /// </remarks>
   [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 more around the better parent by specifying a higher alpha value. 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.")]
-  [StorableClass(StorableClassType.Empty)]
+  [StorableClass]
   public class BlendAlphaBetaCrossover : RealVectorCrossover {
     /// <summary>

trunk/sources/HeuristicLab.Encodings.RealVector/3.3/Crossovers/BlendAlphaCrossover.cs

@@ -37 +37 @@
   /// </remarks>
   [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.")]
-  [StorableClass(StorableClassType.Empty)]
+  [StorableClass]
   public class BlendAlphaCrossover : RealVectorCrossover {
     /// <summary>

trunk/sources/HeuristicLab.Encodings.RealVector/3.3/Crossovers/DiscreteCrossover.cs

@@ -36 +36 @@
   /// </remarks>
   [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.")]
-  [StorableClass(StorableClassType.Empty)]
+  [StorableClass]
   public class DiscreteCrossover : RealVectorCrossover {
     /// <summary>

trunk/sources/HeuristicLab.Encodings.RealVector/3.3/Crossovers/HeuristicCrossover.cs

@@ -35 +35 @@
   /// </remarks>
   [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.")]
-  [StorableClass(StorableClassType.Empty)]
+  [StorableClass]
   public class HeuristicCrossover : RealVectorCrossover {
     /// <summary>

trunk/sources/HeuristicLab.Encodings.RealVector/3.3/Crossovers/LocalCrossover.cs

@@ -33 +33 @@
   /// </remarks>
   [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.")]
-  [StorableClass(StorableClassType.Empty)]
+  [StorableClass]
   public class LocalCrossover : RealVectorCrossover {
     /// <summary>

trunk/sources/HeuristicLab.Encodings.RealVector/3.3/Crossovers/RandomConvexCrossover.cs

@@ -33 +33 @@
   /// </remarks>
   [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.")]
-  [StorableClass(StorableClassType.Empty)]
+  [StorableClass]
   public class RandomConvexCrossover : RealVectorCrossover {
     /// <summary>

trunk/sources/HeuristicLab.Encodings.RealVector/3.3/Crossovers/SimulatedBinaryCrossover.cs

@@ -35 +35 @@
   /// </remarks>
   [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.")]
-  [StorableClass(StorableClassType.Empty)]
+  [StorableClass]
   public class SimulatedBinaryCrossover : RealVectorCrossover {
     /// <summary>

trunk/sources/HeuristicLab.Encodings.RealVector/3.3/Crossovers/SinglePointCrossover.cs

@@ -35 +35 @@
   /// </remarks>
   [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.")]
-  [StorableClass(StorableClassType.Empty)]
+  [StorableClass]
   public class SinglePointCrossover : RealVectorCrossover {
     /// <summary>

trunk/sources/HeuristicLab.Encodings.RealVector/3.3/Crossovers/UniformAllPositionsArithmeticCrossover.cs

@@ -35 +35 @@
   /// </remarks>
   [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. It is implemented as described in Michalewicz, Z. 1999. Genetic Algorithms + Data Structures = Evolution Programs. Third, Revised and Extended Edition, Spring-Verlag Berlin Heidelberg.")]
-  [StorableClass(StorableClassType.Empty)]
+  [StorableClass]
   public class UniformAllPositionsArithmeticCrossover : RealVectorCrossover {
     /// <summary>

trunk/sources/HeuristicLab.Encodings.RealVector/3.3/Crossovers/UniformSomePositionsArithmeticCrossover.cs

@@ -35 +35 @@
   /// </remarks>
   [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.")]
-  [StorableClass(StorableClassType.Empty)]
+  [StorableClass]
   public class UniformSomePositionsArithmeticCrossover : RealVectorCrossover {
     /// <summary>