Hierarchical automatic function definition in genetic programming   [GP]

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Koza, J., R.

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Info: Foundations of Genetic Algorithms 2 (Conference proceedings), 1992, p. 297-318
Keywords:genetic algorithms, genetic programming
Abstract:
A key goal in machine learning [ML] and artificial intelligence [AI] is to automatically and dynamically decompose problems into simpler problems in order to facilitate their solution. This paper describes two extensions to genetic programming, [GP] called "automatic" function definition and "hierarchical automatic" function definition, wherein functions that might be useful in solving a problem are automatically and dynamically defined during a run in terms of dummy variables. The defined functions are then repeatedly called from the automatically discovered "main" result-producing part of the program with different instantiations of the dummy variables. In the "hierarchical" version of automatic function definition, automatically defined functions [ADF] may call other automatically defined functions, [ADF] thus creating a hierarchy of dependencies among the automatically defined functions. [ADF] The even-11-parity problem was solved using hierarchical automatic function definition.
Notes:
Email: Koza@Sunburn.Stanford.edu
Author(s) DL:Online papers for Koza, J., R.
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BibTex:
@InProceedings{Koza92b,
  author =       "John R. Koza",
  title =        "Hierarchical automatic function definition in genetic
                 programming",
  booktitle =    "Foundations of Genetic Algorithms 2",
  editor =       "L. Darrell Whitley",
  publisher =    "Morgan Kaufmann",
  year =         "1992",
  pages =        "297--318",
  address =      "Vail, Colorado, USA",
  month =        "24--29 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Email: Koza@Sunburn.Stanford.edu",
  abstract =     "A key goal in machine learning and artificial
                 intelligence is to automatically and dynamically
                 decompose problems into simpler problems in order to
                 facilitate their solution. This paper describes two
                 extensions to genetic programming, called
                 {"}automatic{"} function definition and {"}hierarchical
                 automatic{"} function definition, wherein functions
                 that might be useful in solving a problem are
                 automatically and dynamically defined during a run in
                 terms of dummy variables. The defined functions are
                 then repeatedly called from the automatically
                 discovered {"}main{"} result-producing part of the
                 program with different instantiations of the dummy
                 variables. In the {"}hierarchical{"} version of
                 automatic function definition, automatically defined
                 functions may call other automatically defined
                 functions, thus creating a hierarchy of dependencies
                 among the automatically defined functions. The
                 even-11-parity problem was solved using hierarchical
                 automatic function definition.",
}