Generalized Genetic Program   [GP]

by

Hafner, C., Froehlich, J. and Gerber, H.

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Info: 1996
Keywords:genetic algorithms, genetic programming
Abstract:
A novel hybrid approach for the Symbolic Regression problem [SR] is presented. First, the classical series expansion approach and the traditional Genetic Programming approach [GP] are outlined. In order to overcome the specific problems of them, a combination is analyzed and two specific implementations are presented. Both the Extended Genetic Programming [GP] and the Generalized Genetic Programming approach [GP] are based on series expansions [SE] with genetic optimizations of the basis functions combined with linear and nonlinear parameter optimizations, but they exhibit important differences in their 'philosophy' and in the details of the implementation. The advantages of our approaches are demonstrated with simple examples that are hard to solve with traditional Genetic Programming. [GP] It is demonstrated that the performance can drastically be improved.
Notes:
postscript generated by MS word appears to be faulty. GGP
URL(s):(G)zipped postscript
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BibTex:
@Unpublished{hafner:1996:GGP,
  author =       "Christian Hafner and Juerg Froehlich and Hansueli
                 Gerber",
  title =        "Generalized Genetic Program",
  note =         "submitted to the 'Evolutionary Computation' Journal",
  year =         "1996",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://alphard.ethz.ch/gp.htm",
  abstract =     "A novel hybrid approach for the Symbolic Regression
                 problem is presented. First, the classical series
                 expansion approach and the traditional Genetic
                 Programming approach are outlined. In order to overcome
                 the specific problems of them, a combination is
                 analyzed and two specific implementations are
                 presented. Both the Extended Genetic Programming and
                 the Generalized Genetic Programming approach are based
                 on series expansions with genetic optimizations of the
                 basis functions combined with linear and nonlinear
                 parameter optimizations, but they exhibit important
                 differences in their 'philosophy' and in the details of
                 the implementation. The advantages of our approaches
                 are demonstrated with simple examples that are hard to
                 solve with traditional Genetic Programming. It is
                 demonstrated that the performance can drastically be
                 improved.",
  notes =        "postscript generated by MS word appears to be faulty.
                 GGP

                 ",
  size =         "25 pages",
}