Controlling Code Growth in Genetic Programming by Mutation   [CG] [GP]

by

Ekart, A.

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Info: Late-Breaking Papers of EuroGP-99 (Conference proceedings), 1999, p. 3-12
Keywords:genetic algorithms, genetic programming
Abstract:
In the paper a method that moderate code growth [CG] in genetic programming [GP] is presented. The addressed problem is symbolic regression. [SR] A special mutation operator [MO] is used for the simplification of programs. If every individual program in each generation is simplified, then performance of the genetic programming system [GP] is worsened. But if simplification is applied as a mutation operator, [MO] more compact solutions of the same or better accuracy can be obtained
Notes:
EuroGP'99LB part of langdon:1999:egplb
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BibTex:
@InProceedings{ekart:1999:ccgGPm,
  author =       "Aniko Ekart",
  title =        "Controlling Code Growth in Genetic Programming by
                 Mutation",
  booktitle =    "Late-Breaking Papers of EuroGP-99",
  year =         "1999",
  editor =       "W. B. Langdon and Riccardo Poli and Peter Nordin and
                 Terry Fogarty",
  pages =        "3--12",
  address =      "Goteborg, Sweeden",
  month =        "26-27 " # may,
  organisation = "EvoGP",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "In the paper a method that moderate code growth in
                 genetic programming is presented. The addressed problem
                 is symbolic regression. A special mutation operator is
                 used for the simplification of programs. If every
                 individual program in each generation is simplified,
                 then performance of the genetic programming system is
                 worsened. But if simplification is applied as a
                 mutation operator, more compact solutions of the same
                 or better accuracy can be obtained",
  notes =        "EuroGP'99LB part of langdon:1999:egplb",
}