AppGP: An Alternative Structural Representation for GP

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{mcphee:1999:AAASRG,
  author =       "Nicholas Freitag McPhee and Nicholas J. Hopper",
  title =        "AppGP: An Alternative Structural Representation for
                 GP",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and 
                 Marc Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "1377--1383",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, models of
                 evolutionary computation, AppGP, local maxima,
                 performance, standard genetic programming, standard
                 subtree crossover, structural convergence",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  DOI =          "doi:10.1109/CEC.1999.782643",
  abstract =     "It has been shown that standard genetic programming
                 using standard subtree crossover is prone to a form of
                 structural convergence which makes it extremely
                 difficult to make changes near the root, occasionally
                 causing runs to become trapped in local maxima. Based
                 on these structural limitations we propose a different
                 tree representation, AppGP, which we hope will avoid
                 this problem in some cases. In this paper, we describe
                 this representation, and compare its performance to the
                 performance of standard GP on a suite of test problems.
                 We find that on all of the test problems, AppGP does no
                 worse than standard GP, and in several it does
                 considerably better, suggesting that the representation
                 warrants further study",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

Genetic Programming entries for Nicholas Freitag McPhee Nicholas J Hopper

Citations