Grammar Based Crossover Operator in Genetic Programming

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

  title =        "Grammar Based Crossover Operator in Genetic
  author =       "Daniel Manrique and Fernando Marquez and Juan Rios and 
                 Alfonso Rodriguez-Paton",
  year =         "2005",
  booktitle =    "Artificial Intelligence and Knowledge Engineering
                 Applications: A Bioinspired Approach: First
                 International Work-Conference on the Interplay Between
                 Natural and Artificial Computation, IWINAC 2005,
                 Proceedings, Part II",
  pages =        "252--261",
  editor =       "Jos{\'e} Mira and Jos{\'e} R. {\'A}lvarez",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3562",
  address =      "Las Palmas, Canary Islands, Spain",
  month =        jun # " 15-18",
  bibdate =      "2005-06-15",
  bibsource =    "DBLP,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-26319-5",
  DOI =          "doi:10.1007/11499305_26",
  size =         "10 pages",
  abstract =     "This paper introduces a new crossover operator for the
                 genetic programming (GP)paradigm, the grammar-based
                 crossover (GBX). This operator works with any
                 grammar-guided genetic programming system. GBX has
                 three important features: it prevents the growth of
                 tree-based GP individuals (a phenomenon known as code
                 bloat), it provides a satisfactory trade-off between
                 the search space exploration and the exploitation
                 capabilities by preserving the context in which
                 subtrees appear in the parent trees and, finally, it
                 takes advantage of the main feature of ambiguous
                 grammars, namely, that there is more than one
                 derivation tree for some sentences (solutions). These
                 features give GBX a high convergence speed and low
                 probability of getting trapped in local optima, as
                 shown throughout the comparison of the results achieved
                 by GBX with other relevant crossover operators in two
                 experiments: a laboratory problem and a real-world
                 task: breast cancer prognosis.",
  notes =        "",

Genetic Programming entries for Daniel Manrique Gamo Fernando Marquez Juan Rios Carrion Alfonso Rodriguez-Paton Aradas