A Quantitative Study of Learning and Generalization in Genetic Programming

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

@InProceedings{castelli:2011:EuroGP,
  author =       "Mauro Castelli and Luca Manzoni and Sara Silva and 
                 Leonardo Vanneschi",
  title =        "A Quantitative Study of Learning and Generalization in
                 Genetic Programming",
  booktitle =    "Proceedings of the 14th European Conference on Genetic
                 Programming, EuroGP 2011",
  year =         "2011",
  month =        "27-29 " # apr,
  editor =       "Sara Silva and James A. Foster and Miguel Nicolau and 
                 Mario Giacobini and Penousal Machado",
  series =       "LNCS",
  volume =       "6621",
  publisher =    "Springer Verlag",
  address =      "Turin, Italy",
  pages =        "25--36",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-20406-7",
  DOI =          "doi:10.1007/978-3-642-20407-4_3",
  abstract =     "The relationship between generalisation and solutions
                 functional complexity in genetic programming (GP) has
                 been recently investigated. Three main contributions
                 are contained in this paper: (1) a new measure of
                 functional complexity for GP solutions, called Graph
                 Based Complexity (GBC) is defined and we show that it
                 has a higher correlation with GP performance on
                 out-of-sample data than another complexity measure
                 introduced in a recent publication. (2) A new measure
                 is presented, called Graph Based Learning Ability
                 (GBLA). It is inspired by the GBC and its goal is to
                 quantify the ability of GP to learn difficult training
                 points; we show that GBLA is negatively correlated with
                 the performance of GP on out-of-sample data. (3)
                 Finally, we use the ideas that have inspired the
                 definition of GBC and GBLA to define a new fitness
                 function, whose suitability is empirically
                 demonstrated. The experimental results reported in this
                 paper have been obtained using three real-life
                 multidimensional regression problems.",
  notes =        "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
                 conjunction with EvoCOP2011 EvoBIO2011 and
                 EvoApplications2011",
}

Genetic Programming entries for Mauro Castelli Luca Manzoni Sara Silva Leonardo Vanneschi

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