A New Implementation of Geometric Semantic GP and its Application to Problems in Pharmacokinetics

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@InProceedings{vanneschi:2013:EuroGP,
  author =       "Leonardo Vanneschi and Mauro Castelli and 
                 Luca Manzoni and Sara Silva",
  title =        "A New Implementation of Geometric Semantic GP and its
                 Application to Problems in Pharmacokinetics",
  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 =        "205--216",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-37206-3",
  DOI =          "doi:10.1007/978-3-642-37207-0_18",
  abstract =     "Moraglio et al. have recently introduced new genetic
                 operators for genetic programming, called geometric
                 semantic operators. These operators induce a unimodal
                 fitness landscape for all the problems consisting in
                 matching input data with known target outputs (like
                 regression and classification). This feature
                 facilitates genetic programming evolvability, which
                 makes these operators extremely promising.
                 Nevertheless, Moraglio et al. leave open problems, the
                 most important one being the fact that these operators,
                 by construction, always produce offspring that are
                 larger than their parents, causing an exponential
                 growth in the size of the individuals, which actually
                 renders them useless in practice. In this paper we
                 overcome this limitation by presenting a new efficient
                 implementation of the geometric semantic operators.
                 This allows us, for the first time, to use them on
                 complex real-life applications, like the two problems
                 in pharmacokinetics that we address here. Our results
                 confirm the excellent evolvability of geometric
                 semantic operators, demonstrated by the good results
                 obtained on training data. Furthermore, we have also
                 achieved a surprisingly good generalisation ability, a
                 fact that can be explained considering some properties
                 of geometric semantic operators, which makes them even
                 more appealing than before.",
  notes =        "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in
                 conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013
                 and EvoApplications2013",
}

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

Citations