Genetic Algorithms Using Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  title =        "Genetic Algorithms Using Grammatical Evolution",
  author =       "Conor Ryan and Miguel Nicolau and Michael O'Neill",
  editor =       "James A. Foster and Evelyne Lutton and 
                 Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "278--287",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  organisation = "EvoNet",
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, gauge",
  ISBN =         "3-540-43378-3",
  DOI =          "doi:10.1007/3-540-45984-7_27",
  abstract =     "This paper describes the GAUGE system, Genetic
                 Algorithms Using Grammatical Evolution. GAUGE is a
                 position independent Genetic Algorithm that uses
                 Grammatical Evolution with an attribute grammar to
                 dictate what position a gene codes for. GAUGE suffers
                 from neither under-specification nor
                 over-specification, is guaranteed to produce
                 syntactically correct individuals, and does not require
                 any repair after the application of genetic operators.
                 GAUGE is applied to the standard onemax problem, with
                 results showing that its genotype to phenotype mapping
                 and position independence nature do not affect its
                 performance as a normal genetic algorithm. A new
                 problem is also presented, a deceptive version of the
                 Mastermind game, and we show that GAUGE possesses the
                 position independence characteristics it claims, and
                 outperforms several genetic algorithms, including the
                 competent genetic algorithm messy GA.",
  notes =        "EuroGP'2002, part of \cite{lutton:2002:GP}",

Genetic Programming entries for Conor Ryan Miguel Nicolau Michael O'Neill