The Generalisation Ability of a Selection Architecture for Genetic Programming

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

  author =       "David Jackson",
  title =        "The Generalisation Ability of a Selection Architecture
                 for Genetic Programming",
  booktitle =    "Parallel Problem Solving from Nature - PPSN X",
  year =         "2008",
  editor =       "Gunter Rudolph and Thomas Jansen and Simon Lucas and 
                 Carlo Poloni and Nicola Beume",
  volume =       "5199",
  series =       "LNCS",
  pages =        "468--477",
  address =      "Dortmund",
  month =        "13-17 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-87699-5",
  DOI =          "doi:10.1007/978-3-540-87700-4_47",
  abstract =     "As an alternative to various existing approaches to
                 incorporating modular decomposition and reuse in
                 genetic programming (GP), we have proposed a new method
                 for hierarchical evolution. Based on a division of the
                 problem's test case inputs into subsets, it employs a
                 program structure that we refer to as a selection
                 architecture. Although the performance of GP systems
                 based on this architecture has been shown to be
                 superior to that of conventional systems, the nature of
                 evolved programs is radically different, leading to
                 speculation as to how well such programs may generalise
                 to deal with previously unseen inputs. We have
                 therefore performed additional experimentation to
                 evaluate the approach's generalisation ability, and
                 have found that it seems to stand up well against
                 standard GP in this regard.",
  notes =        "PPSN X",

Genetic Programming entries for David Jackson