Searching for Novel Classifiers

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{naredo:2013:EuroGP,
  author =       "Enrique Naredo and Leonardo Trujillo and 
                 Yuliana Martinez",
  title =        "Searching for Novel Classifiers",
  booktitle =    "Proceedings of the 16th European Conference on Genetic
                 Programming, EuroGP 2013",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and 
                 A. Sima Uyar and Bin Hu",
  series =       "LNCS",
  volume =       "7831",
  publisher =    "Springer Verlag",
  address =      "Vienna, Austria",
  pages =        "145--156",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Novelty
                 Search, Classification",
  isbn13 =       "978-3-642-37206-3",
  DOI =          "doi:10.1007/978-3-642-37207-0_13",
  abstract =     "Natural evolution is an open-ended search process
                 without an a priori fitness function that needs to be
                 optimised. On the other hand, evolutionary algorithms
                 (EAs) rely on a clear and quantitative objective. The
                 Novelty Search algorithm (NS) substitutes fitness-based
                 selection with a novelty criteria; i.e., individuals
                 are chosen based on their uniqueness. To do so,
                 individuals are described by the behaviours they
                 exhibit, instead of their phenotype or genetic content.
                 NS has mostly been used in evolutionary robotics, where
                 the concept of behavioural space can be clearly
                 defined. Instead, this work applies NS to a more
                 general problem domain, classification. To this end,
                 two behavioral descriptors are proposed, each
                 describing a classifier's performance from two
                 different perspectives. Experimental results show that
                 NS-based search can be used to derive effective
                 classifiers. In particular, NS is best suited to solve
                 difficult problems, where exploration needs to be
                 encouraged and maintained.",
  notes =        "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in
                 conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013
                 and EvoApplications2013",
}

Genetic Programming entries for Enrique Naredo Leonardo Trujillo Yuliana Martinez

Citations