Accuracy in Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Michael F. Korns",
  title =        "Accuracy in Symbolic Regression",
  booktitle =    "Genetic Programming Theory and Practice IX",
  year =         "2011",
  editor =       "Rick Riolo and Ekaterina Vladislavleva and 
                 Jason H. Moore",
  series =       "Genetic and Evolutionary Computation",
  address =      "Ann Arbor, USA",
  month =        "12-14 " # may,
  publisher =    "Springer",
  chapter =      "8",
  pages =        "129--151",
  keywords =     "genetic algorithms, genetic programming, Abstract
                 Expression Grammars, Differential Evolution, Grammar
                 Template, Particle Swarm, Symbolic Regression",
  isbn13 =       "978-1-4614-1769-9",
  DOI =          "doi:10.1007/978-1-4614-1770-5_8",
  abstract =     "This chapter asserts that, in current state-of-the-art
                 symbolic regression engines, accuracy is poor. That is
                 to say that state-of-the-art symbolic regression
                 engines return a champion with good fitness; however,
                 obtaining a champion with the correct formula is not
                 forthcoming even in cases of only one basis function
                 with minimally complex grammar depth.

                 Ideally, users expect that for test problems created
                 with no noise, using only functions in the specified
                 grammar, with only one basis function and some minimal
                 grammar depth, that state-of-the-art symbolic
                 regression systems should return the exact formula (or
                 at least an isomorph) used to create the test data.
                 Unfortunately, this expectation cannot currently be
                 achieved using published state-of-the-art symbolic
                 regression techniques.

                 Several classes of test formulas, which prove
                 intractable, are examined and an understanding of why
                 they are intractable is developed. Techniques in
                 Abstract Expression Grammars are employed to render
                 these problems tractable, including manipulation of the
                 epigenome during the evolutionary process, together
                 with breeding of multiple targeted epigenomes in
                 separate population islands.

                 A selected set of currently intractable problems are
                 shown to be solvable, using these techniques, and a
                 proposal is put forward for a discipline-wide program
                 of improving accuracy in state-of-the-art symbolic
                 regression systems.",
  notes =        "part of \cite{Riolo:2011:GPTP}",
  affiliation =  "Korns Associates, 98 Perea Street, Makati, 1229
                 Manila, Philippines",

Genetic Programming entries for Michael Korns