Extreme Accuracy in Symbolic Regression

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

@InCollection{Korns:2013:GPTP,
  author =       "Michael F. Korns",
  title =        "Extreme Accuracy in Symbolic Regression",
  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 =      "1",
  pages =        "1--30",
  address =      "Ann Arbor, USA",
  month =        "9-11 " # may,
  keywords =     "genetic algorithms, genetic programming, Abstract
                 expression grammars, Grammar template, Particle swarm,
                 PSO, Symbolic regression",
  isbn13 =       "978-1-4939-0374-0",
  DOI =          "doi:10.1007/978-1-4939-0375-7_1",
  abstract =     "Although recent advances in symbolic regression (SR)
                 have promoted the field into the early stages of
                 commercial exploitation, the poor accuracy of SR is
                 still plaguing even the most advanced commercial
                 packages today. Users expect to have the correct
                 formula returned, especially in cases with zero noise
                 and only one basis function with minimally complex
                 grammar depth. Poor accuracy is a hindrance to greater
                 academic and industrial acceptance of SR tools.

                 In a previous paper, the poor accuracy of Symbolic
                 Regression was explored, and several classes of test
                 formulae, which prove intractable for SR, were
                 examined. An understanding of why these test problems
                 prove intractable was developed. In another paper a
                 baseline Symbolic Regression algorithm was developed
                 with specific techniques for optimising embedded real
                 numbers constants. These previous steps have placed us
                 in a position to make an attempt at vanquishing the SR
                 accuracy problem.

                 In this chapter we develop a complex algorithm for
                 modern symbolic regression which is extremely accurate
                 for a large class of Symbolic Regression problems. The
                 class of problems, on which SR is extremely accurate,
                 is described in detail. A definition of extreme
                 accuracy is provided, and an informal argument of
                 extreme SR accuracy is outlined in this chapter. Given
                 the critical importance of accuracy in SR, it is our
                 suspicion that in the future all commercial Symbolic
                 Regression packages will use this algorithm or a
                 substitute for this algorithm.",
  notes =        "http://cscs.umich.edu/gptp-workshops/

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

Genetic Programming entries for Michael Korns

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