Geometric Semantic Genetic Programming is Overkill

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

@InProceedings{Pawlak:2016:EuroGP,
  author =       "Tomasz P. Pawlak",
  title =        "Geometric Semantic Genetic Programming is Overkill",
  booktitle =    "EuroGP 2016: Proceedings of the 19th European
                 Conference on Genetic Programming",
  year =         "2016",
  month =        "30 " # mar # "--1 " # apr,
  editor =       "Malcolm I. Heywood and James McDermott and 
                 Mauro Castelli and Ernesto Costa and Kevin Sim",
  series =       "LNCS",
  volume =       "9594",
  publisher =    "Springer Verlag",
  address =      "Porto, Portugal",
  pages =        "246--260",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-30668-1",
  DOI =          "doi:10.1007/978-3-319-30668-1_16",
  abstract =     "Recently, a new notion of Geometric Semantic Genetic
                 Programming emerged in the field of automatic program
                 induction from examples. Given that the induction
                 problem is stated by means of function learning and a
                 fitness function is a metric, GSGP uses geometry of
                 solution space to search for the optimal program. We
                 demonstrate that a program constructed by GSGP is
                 indeed a linear combination of random parts. We also
                 show that this type of program can be constructed in a
                 predetermined time by much simpler algorithm and with
                 guarantee of solving the induction problem optimally.
                 We experimentally compare the proposed algorithm to
                 GSGP on a set of symbolic regression, Boolean function
                 synthesis and classifier induction problems. The
                 proposed algorithm is superior to GSGP in terms of
                 training-set fitness, size of produced programs and
                 computational cost, and generalizes on test-set
                 similarly to GSGP.",
  notes =        "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in
                 conjunction with EvoCOP2016, EvoMusArt2016 and
                 EvoApplications2016",
}

Genetic Programming entries for Tomasz Pawlak

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