Representations for Evolutionary Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Franz Rothlauf",
  title =        "Representations for Evolutionary Algorithms",
  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 =        "413--434",
  address =      "Denver, Colorado, USA",
  month =        "20-24 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  organisation = "SIGEVO",
  DOI =          "doi:10.1145/2908961.2926981",
  publisher =    "ACM",
  note =         "tutorial",
  publisher_address = "New York, NY, USA",
  abstract =     "Successful and efficient use of evolutionary
                 algorithms (EA) depends on the choice of the genotype,
                 the problem representation (mapping from genotype to
                 phenotype) and on the choice of search operators that
                 are applied to the genotypes. These choices cannot be
                 made independently of each other. The question whether
                 a certain representation leads to better performing EAs
                 than an alternative representation can only be answered
                 when the operators applied are taken into
                 consideration. The reverse is also true: deciding
                 between alternative operators is only meaningful for a
                 given representation.

                 In EA practice one can distinguish two complementary
                 approaches. The first approach uses indirect
                 representations where a solution is encoded in a
                 standard data structure, such as strings, vectors, or
                 discrete permutations, and standard off-the-shelf
                 search operators are applied to these genotypes. This
                 is for example the case in standard genetic algorithms,
                 evolution strategies, and some genetic programming
  notes =        "Distributed at GECCO-2016.",

Genetic Programming entries for Franz Rothlauf