Searching for novel clustering programs

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

  author =       "Enrique Naredo and Leonardo Trujillo",
  title =        "Searching for novel clustering programs",
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and Anne Auger and 
                 Jaume Bacardit and Josh Bongard and Juergen Branke and 
                 Nicolas Bredeche and Dimo Brockhoff and 
                 Francisco Chicano and Alan Dorin and Rene Doursat and 
                 Aniko Ekart and Tobias Friedrich and Mario Giacobini and 
                 Mark Harman and Hitoshi Iba and Christian Igel and 
                 Thomas Jansen and Tim Kovacs and Taras Kowaliw and 
                 Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and 
                 John McCall and Alberto Moraglio and 
                 Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and 
                 Gustavo Olague and Yew-Soon Ong and 
                 Michael E. Palmer and Gisele Lobo Pappa and 
                 Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and 
                 Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and 
                 Daniel Tauritz and Leonardo Vanneschi",
  isbn13 =       "978-1-4503-1963-8",
  pages =        "1093--1100",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463505",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Novelty search (NS) is an open-ended evolutionary
                 algorithm that eliminates the need for an explicit
                 objective function. Instead, NS focuses selective
                 pressure on the search for novel solutions. NS has
                 produced intriguing results in specialised domains, but
                 has not been applied in most machine learning areas.
                 The key component of NS is that each individual is
                 described by the behaviour it exhibits, and this
                 description is used to determine how novel each
                 individual is with respect to what the search has
                 produced thus far. However, describing individuals in
                 behavioural space is not trivial, and care must be
                 taken to properly define a descriptor for a particular
                 domain. This paper applies NS to a mainstream pattern
                 analysis area: data clustering. To do so, a descriptor
                 of clustering performance is proposed and tested on
                 several problems, and compared with two control
                 methods, Fuzzy C-means and K-means. Results show that
                 NS can effectively be applied to data clustering in
                 some circumstances. NS performance is quite poor on
                 simple or easy problems, achieving basically random
                 performance. Conversely, as the problems get harder NS
                 performs better, and outperforming the control methods.
                 It seems that the search space exploration induced by
                 NS is fully exploited only when generating good
                 solutions is more challenging.",
  notes =        "Also known as \cite{2463505} GECCO-2013 A joint
                 meeting of the twenty second international conference
                 on genetic algorithms (ICGA-2013) and the eighteenth
                 annual genetic programming conference (GP-2013)",

Genetic Programming entries for Enrique Naredo Leonardo Trujillo