Improving Grammer Based Evolution Algorithms via Attributed Derivation Trees

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Szilvia Zvada and Robert Vanyi",
  title =        "Improving Grammer Based Evolution Algorithms via
                 Attributed Derivation Trees",
  booktitle =    "Genetic Programming 7th European Conference, EuroGP
                 2004, Proceedings",
  year =         "2004",
  editor =       "Maarten Keijzer and Una-May O'Reilly and 
                 Simon M. Lucas and Ernesto Costa and Terence Soule",
  volume =       "3003",
  series =       "LNCS",
  pages =        "208--219",
  address =      "Coimbra, Portugal",
  publisher_address = "Berlin",
  month =        "5-7 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-21346-5",
  URL =          "",
  DOI =          "doi:10.1007/978-3-540-24650-3_19",
  abstract =     "Using Genetic Programming difficult optimisation
                 problems can be solved, even if the candidate solutions
                 are complex objects. In such cases, it is a costly
                 procedure to correct or replace the invalid individuals
                 that may appear during the evolutionary process.
                 Instead of such post-processing, context-free grammars
                 can be used to describe the syntax of valid solutions,
                 and the algorithm can be modified to work on derivation
                 trees, such that it does not generate invalid
                 individuals. Although tree operators have the advantage
                 of good parameterizability, it is not trivial to
                 construct them correctly and efficiently. An existing
                 method for derivation tree evolution and its extension
                 towards attributed derivation trees are discussed. As
                 the result of this extension the operators are not only
                 faster but they are easy to parameterise, moreover the
                 algorithm is better guided, thus it can converge
  notes =        "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
                 conjunction with EvoCOP2004 and EvoWorkshops2004",

Genetic Programming entries for Szilvia Zvada Robert Vanyi