A Relaxed Approach to Simplification in Genetic Programming

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

  author =       "Mark Johnston and Thomas Liddle and Mengjie Zhang",
  title =        "A Relaxed Approach to Simplification in Genetic
  booktitle =    "Proceedings of the 13th European Conference on Genetic
                 Programming, EuroGP 2010",
  year =         "2010",
  editor =       "Anna Isabel Esparcia-Alcazar and Aniko Ekart and 
                 Sara Silva and Stephen Dignum and A. Sima Uyar",
  volume =       "6021",
  series =       "LNCS",
  pages =        "110--121",
  address =      "Istanbul",
  month =        "7-9 " # apr,
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-12147-0",
  DOI =          "doi:10.1007/978-3-642-12148-7_10",
  abstract =     "We propose a novel approach to program simplification
                 in tree-based Genetic Programming, based upon numerical
                 relaxations of algebraic rules. We also separate
                 proposal of simplifications from an acceptance
                 criterion that checks the effect of proposed
                 simplifications on the evaluation of training examples,
                 looking several levels up the tree. We test our
                 simplification method on three classification datasets
                 and conclude that the success of linear regression is
                 dataset dependent, that looking further up the tree can
                 catch ineffective simplifications, and that CPU time
                 can be significantly reduced while maintaining
                 classification accuracy on unseen examples.",
  notes =        "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010
                 held in conjunction with EvoCOP2010 EvoBIO2010 and

Genetic Programming entries for Mark Johnston Thomas Liddle Mengjie Zhang