Automatic Creation of Taxonomies of Genetic Programming Systems

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

@InProceedings{Graff:2009:eurogp,
  author =       "Mario Graff and Riccardo Poli",
  title =        "Automatic Creation of Taxonomies of Genetic
                 Programming Systems",
  booktitle =    "Proceedings of the 12th European Conference on Genetic
                 Programming, EuroGP 2009",
  year =         "2009",
  editor =       "Leonardo Vanneschi and Steven Gustafson and 
                 Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner",
  volume =       "5481",
  series =       "LNCS",
  pages =        "145--158",
  address =      "Tuebingen",
  month =        apr # " 15-17",
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-01180-1",
  DOI =          "doi:10.1007/978-3-642-01181-8_13",
  size =         "14 pages",
  abstract =     "A few attempts to create taxonomies in evolutionary
                 computation have been made. These either group
                 algorithms or group problems on the basis of their
                 similarities. Similarity is typically evaluated by
                 manually analysing algorithms/problems to identify key
                 characteristics that are then used as a basis to form
                 the groups of a taxonomy. This task is not only very
                 tedious but it is also rather subjective. As a
                 consequence the resulting taxonomies lack universality
                 and are sometimes even questionable. In this paper we
                 present a new and powerful approach to the construction
                 of taxonomies and we apply it to Genetic Programming
                 (GP). Only one manually constructed taxonomy of
                 problems has been proposed in GP before, while no GP
                 algorithm taxonomy has ever been suggested. Our
                 approach is entirely automated and objective. We apply
                 it to the problem of grouping GP systems with their
                 associated parameter settings. We do this on the basis
                 of performance signatures which represent the behaviour
                 of each system across a class of problems. These
                 signatures are obtained thorough a process which
                 involves the instantiation of models of GP's
                 performance.We test the method on a large class of
                 Boolean induction problems.",
  notes =        "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in
                 conjunction with EvoCOP2009, EvoBIO2009 and
                 EvoWorkshops2009",
}

Genetic Programming entries for Mario Graff Guerrero Riccardo Poli

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