Code Fragments: Past and Future use in Transfer Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Browne:2016:GECCOcomp,
  author =       "Will N. Browne",
  title =        "Code Fragments: Past and Future use in Transfer
                 Learning",
  booktitle =    "GECCO '16 Companion: Proceedings of the Companion
                 Publication of the 2016 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  isbn13 =       "978-1-4503-4323-7",
  pages =        "1405--1405",
  address =      "Denver, Colorado, USA",
  month =        "20-24 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  organisation = "SIGEVO",
  DOI =          "doi:10.1145/2908961.2931737",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Code Fragments (CFs) have existed as an extension to
                 Evolutionary Computation, specifically Learning
                 Classifiers Systems (LCSs), for half a decade. Through
                 the scaling, abstraction and reuse of both knowledge
                 and functionality that CFs enable, interesting problems
                 have been solved beyond the capability of any other
                 technique. This paper traces the development of the
                 different CF-based systems and outlines future research
                 directions that will form the basis for advanced
                 Transfer Learning in LCSs.",
  notes =        "Distributed at GECCO-2016.",
}

Genetic Programming entries for Will N Browne

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