Learning Schemes for Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Esparcia-Alcazar:1997:lsGP,
  author =       "Anna I. Esparcia-Alcazar and Ken Sharman",
  title =        "Learning Schemes for Genetic Programming",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "57--65",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  URL =          "http://www.iti.upv.es/~anna/papers/learningGP97.ps",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/esparcia-alcazar/Esparcia-Alcazar_1997_lsGP.pdf",
  size =         "9 pages",
  abstract =     "A learning capability is introduced in the Genetic
                 Programming (GP) paradigm. This is achieved by
                 enhancing GP with Simulated Annealing (SA), where the
                 latter adapts the parameter values (in the form of node
                 gains) in the structures evolved by the former. A
                 special feature of this approach is that, due to the
                 particularities of the representation used, it allows
                 engineering problems (in which numerical parameters are
                 important) to be addressed, thus extending the
                 applicability of the GP paradigm.

                 We study two different learning schemes, which we refer
                 to as Darwinian and Lamarckian according to whether the
                 learned node gains are inherited or not. We compare the
                 results obtained by these two techniques to those
                 obtained in the absence of learning (both with node
                 gain representation and standard GP representation).
                 The results show the great interest of both learning
                 schemes.

                 The application presented is a classical Digital Signal
                 Processing problem: the equalisation of a noisy
                 communications channel.",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

Genetic Programming entries for Anna Esparcia-Alcazar Kenneth C Sharman

Citations