Improving the Generalisation Ability of Genetic Programming with Semantic Similarity based Crossover

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

@InProceedings{Nguyen:2010:EuroGP,
  author =       "Nguyen Quang Uy and Nguyen Thi Hien and 
                 Nguyen Xuan Hoai and Michael O'Neill",
  title =        "Improving the Generalisation Ability of Genetic
                 Programming with Semantic Similarity based Crossover",
  booktitle =    "Proceedings of the 13th European Conference on Genetic
                 Programming, EuroGP 2010",
  year =         "2010",
  editor =       "Anna Isabel Esparcia-Alcazar and Aniko Ekart and 
                 Sara Silva and Stephen Dignum and A. Sima Uyar",
  volume =       "6021",
  series =       "LNCS",
  pages =        "184--195",
  address =      "Istanbul",
  month =        "7-9 " # apr,
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Semantics,
                 Generalisation, Crossover",
  isbn13 =       "978-3-642-12147-0",
  DOI =          "doi:10.1007/978-3-642-12148-7_16",
  size =         "12 pages",
  abstract =     "This paper examines the impact of semantic control on
                 the ability of Genetic Programming (GP) to generalise
                 via a semantic based crossover operator (Semantic
                 Similarity based Crossover - SSC). The use of
                 validation sets is also investigated for both standard
                 crossover and SSC. All GP systems are tested on a
                 number of real-valued symbolic regression problems. The
                 experimental results show that while using validation
                 sets barely improve generalisation ability of GP,by
                 using semantics, the performance of Genetic Programming
                 is enhanced both on training and testing data. Further
                 recorded statistics shows that the size of the evolved
                 solutions by using SSC are often smaller than ones
                 obtained from GP systems that do not use semantics.
                 This can be seen as one of the reasons for the success
                 of SSC in improving the generalisation ability of GP.",
  notes =        "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010
                 held in conjunction with EvoCOP2010 EvoBIO2010 and
                 EvoApplications2010",
}

Genetic Programming entries for Quang Uy Nguyen Nguyen Thi Hien Nguyen Xuan Hoai Michael O'Neill

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