Undirected Training of Run Transferable Libraries

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{eurogp:KeijzerRMC05,
  author =       "Maarten Keijzer and Conor Ryan and Gearoid Murphy and 
                 Mike Cattolico",
  editor =       "Maarten Keijzer and Andrea Tettamanzi and 
                 Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
  title =        "Undirected Training of Run Transferable Libraries",
  booktitle =    "Proceedings of the 8th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3447",
  year =         "2005",
  address =      "Lausanne, Switzerland",
  month =        "30 " # mar # " - 1 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-25436-6",
  pages =        "361--370",
  DOI =          "doi:10.1007/b107383",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "This paper investigates the robustness of Run
                 Transferable Libraries(RTLs) on scaled problems. RTLs
                 are provide GP with a library of functions which
                 replace the usual primitive functions provided when
                 approaching a problem. The RTL evolves from run to run
                 using feedback based on function usage, and has been
                 shown to outperform GP by an order of magnitude on a
                 variety of scalable problems. RTLs can, however, also
                 be applied across a {em domain} of related problems, as
                 well as across a range of scaled instances of a single
                 problem. To this successfully, it will need to balance
                 a range of functions. We introduce a problem that can
                 deceive the system into converging to a sub-optimal set
                 of functions, and demonstrate that this is a
                 consequence of the greediness of the library update
                 algorithm. We demonstrate that a much simpler, truly
                 evolutionary, update strategy doesn't suffer from this
                 problem, and exhibits far better optimisation
                 properties than the original strategy.",
  notes =        "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
                 conjunction with EvoCOP2005 and EvoWorkshops2005",
}

Genetic Programming entries for Maarten Keijzer Conor Ryan Gearoid Murphy Mike Cattolico

Citations