Highly Accurate Symbolic Regression with Noisy Training Data

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Michael Korns",
  title =        "Highly Accurate Symbolic Regression with Noisy
                 Training Data",
  booktitle =    "Genetic Programming Theory and Practice XIII",
  year =         "2015",
  editor =       "Rick Riolo and William P. Worzel and M. Kotanchek and 
                 A. Kordon",
  series =       "Genetic and Evolutionary Computation",
  pages =        "91--115",
  address =      "Ann Arbor, USA",
  month =        "14-16 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 regression, Abstract expression grammars, Grammar
                 template genetic programming, Particle swarm",
  isbn13 =       "978-3-319-34223-8",
  URL =          "http://www.springer.com/us/book/9783319342214",
  DOI =          "doi:10.1007/978-3-319-34223-8_6",
  abstract =     "As symbolic regression (SR) has advanced into the
                 early stages of commercial exploitation, the poor
                 accuracy of SR, still plaguing even the most advanced
                 commercial packages, has become an issue for early
                 adopters. Users expect to have the correct formula
                 returned, especially in cases with zero noise and only
                 one basis function with minimally complex grammar
  notes =        "http://cscs.umich.edu/gptp-workshops/

                 Part of \cite{Riolo:2015:GPTP} Published after the
                 workshop in 2016",

Genetic Programming entries for Michael Korns