Simultaneous Synthesis of Multiple Functions using Genetic Programming with Scaffolding

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Iwo Bladek and Krzysztof Krawiec",
  title =        "Simultaneous Synthesis of Multiple Functions using
                 Genetic Programming with Scaffolding",
  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",
  pages =        "97--98",
  keywords =     "genetic algorithms, genetic programming, scaffolding,
                 multisynthesis, problem decomposition, Scala: Poster",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4323-7",
  poster_url =   "",
  DOI =          "doi:10.1145/2908961.2908992",
  size =         "2 pages",
  abstract =     "We consider simultaneous evolutionary synthesis of
                 multiple functions, and verify whether such approach
                 leads to computational savings compared to conventional
                 synthesis of functions one-by-one. We also extend the
                 proposed synthesis model with scaffolding, a technique
                 originally intended to facilitate evolution of
                 recursive programs \cite{Moraglio:2012:CEC}, and
                 consisting in fetching the desired output from a test
                 case, rather than calling a function. Experiment
                 concerning synthesis of list manipulation programs in
                 Scala allows us to conclude that parallel synthesis
                 indeed pays off, and that engagement of scaffolding
                 leads to further improvements.",
  notes =        "NSGA-II

                 Distributed at GECCO-2016.",

Genetic Programming entries for Iwo Bladek Krzysztof Krawiec