Geometric semantic genetic programming for biomedical applications: A state of the art upgrade

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

@InProceedings{vanneschi:2017:CECb,
  author =       "Leonardo Vanneschi and Mauro Castelli and 
                 Ivo Goncalves and Luca Manzoni and Sara Silva",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "Geometric semantic genetic programming for biomedical
                 applications: A state of the art upgrade",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "177--184",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "Geometric semantic genetic programming is a hot topic
                 in evolutionary computation and recently it has been
                 used with success on several problems from Biology and
                 Medicine. Given the young age of geometric semantic
                 genetic programming, in the last few years theoretical
                 research, aimed at improving the method, and
                 applicative research proceeded rapidly and in parallel.
                 As a result, the current state of the art is confused
                 and presents some “holes”. For instance, some
                 recent improvements of geometric semantic genetic
                 programming have never been applied to some popular
                 biomedical applications. The objective of this paper is
                 to fill this gap. We consider the biomedical
                 applications that have more frequently been used by
                 genetic programming researchers in the last few years
                 and we systematically test, in a consistent way, using
                 the same parameter settings and configurations, all the
                 most popular existing variants of geometric semantic
                 genetic programming on all those applications.
                 Analysing all these results, we obtain a much more
                 homogeneous and clearer picture of the state of the
                 art, that allows us to draw stronger conclusions.",
  keywords =     "genetic algorithms, genetic programming, medical
                 computing, biomedical applications, evolutionary
                 computation, geometric semantic genetic programming,
                 parameter settings, Drugs, Electronic mail, GSM,
                 Proteins, Semantics",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969311",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as
                 \cite{7969311}",
}

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

Citations