Geometric Semantic Genetic Programming for Real Life Applications

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

@InCollection{Vanneschi:2013:GPTP,
  author =       "Leonardo Vanneschi and Sara Silva and 
                 Mauro Castelli and Luca Manzoni",
  title =        "Geometric Semantic Genetic Programming for Real Life
                 Applications",
  booktitle =    "Genetic Programming Theory and Practice XI",
  year =         "2013",
  series =       "Genetic and Evolutionary Computation",
  editor =       "Rick Riolo and Jason H. Moore and Mark Kotanchek",
  publisher =    "Springer",
  chapter =      "11",
  pages =        "191--209",
  address =      "Ann Arbor, USA",
  month =        "9-11 " # may,
  keywords =     "genetic algorithms, genetic programming, Geometric
                 semantic operators, Fitness landscapes, Overfitting,
                 Parameter tuning",
  isbn13 =       "978-1-4939-0374-0",
  DOI =          "doi:10.1007/978-1-4939-0375-7_11",
  abstract =     "In a recent contribution we have introduced a new
                 implementation of geometric semantic operators for
                 Genetic Programming. Thanks to this implementation, we
                 are now able to deeply investigate their usefulness and
                 study their properties on complex real-life
                 applications. Our experiments confirm that these
                 operators are more effective than traditional ones in
                 optimising training data, due to the fact that they
                 induce a unimodal fitness landscape. Furthermore, they
                 automatically limit over fitting, something we had
                 already noticed in our recent contribution, and that is
                 further discussed here. Finally, we investigate the
                 influence of some parameters on the effectiveness of
                 these operators, and we show that tuning their values
                 and setting them a-priori may be wasted effort.
                 Instead, if we randomly modify the values of those
                 parameters several times during the evolution, we
                 obtain a performance that is comparable with the one
                 obtained with the best setting, both on training and
                 test data for all the studied problems.",
  notes =        "http://cscs.umich.edu/gptp-workshops/

                 Part of \cite{Riolo:2013:GPTP} published after the
                 workshop in 2013",
}

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

Citations