Efficient indexing of similarity models with inequality symbolic regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Tomas Bartos and Tomas Skopal and Juraj Mosko",
  title =        "Efficient indexing of similarity models with
                 inequality symbolic regression",
  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 =        "901--908",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463487",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The increasing amount of available unstructured
                 content introduced a new concept of searching for
                 information, the content-based retrieval. The principle
                 behind is that the objects are compared based on their
                 content which is far more complex than simple text or
                 metadata based searching. Many indexing techniques
                 arose to provide an efficient and effective similarity
                 searching. However, these methods are restricted to a
                 specific domain such as the metric space model. If this
                 prerequisite is not fulfilled, indexing cannot be used,
                 while each similarity search query degrades to
                 sequential scanning which is unacceptable for large
                 datasets. Inspired by previous successful results, we
                 decided to apply the principles of genetic programming
                 to the area of database indexing. We developed the
                 GP-SIMDEX which is a universal framework that is
                 capable of finding precise and efficient indexing
                 methods for similarity searching for any given
                 similarity data. For this purpose, we introduce the
                 inequality symbolic regression principle and show how
                 it helps the GP-SIMDEX Framework to find appropriate
                 results that in most cases outperform the best-known
                 indexing methods.",
  notes =        "Also known as \cite{2463487} 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 Tomas Bartos Tomas Skopal Juraj Mosko