Search Strategies for Grammatical Optimization Problems - Alternatives to Grammar-Guided Genetic Programming

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

  author =       "Gabriel Kronberger and Michael Kommenda",
  title =        "Search Strategies for Grammatical Optimization
                 Problems - Alternatives to Grammar-Guided Genetic
  bibdate =      "2015-06-29",
  bibsource =    "DBLP,
  booktitle =    "Computational Intelligence and Efficiency in
                 Engineering Systems",
  publisher =    "Springer",
  year =         "2015",
  volume =       "595",
  editor =       "Grzegorz Borowik and Zenon Chaczko and 
                 Witold Jacak and Tadeusz Luba",
  isbn13 =       "978-3-319-15719-1",
  pages =        "89--102",
  series =       "Studies in Computational Intelligence",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  DOI =          "doi:10.1007/978-3-319-15720-7_7",
  abstract =     "In this chapter, we have a closer look at search
                 strategies for optimization problems, where the
                 structure of valid solutions is defined through a
                 formal grammar. These problems frequently occur in the
                 genetic programming (GP) literature, especially in the
                 context of grammar-guided genetic programming [18].
                 Even though a lot of progress has been made to extend
                 and improve GP in the last 25 years and many impressive
                 solutions have been produced by GP, the initial goal of
                 an automated programming machine for generating
                 computer programs is still far away and GP is not yet
                 established as a reliable and general method for
                 solving grammatical optimization problems. Instead,
                 many different GP variants have been described and used
                 for solving specific problems. Today the term GP refers
                 to a large set of related algorithms where the
                 commonality mainly is that an evolutionary algorithm is
                 used to produce solutions which often—but not
                 always—represent code that can be executed by a
                 problem specific...",

Genetic Programming entries for Gabriel Kronberger Michael Kommenda