Learning Dynamical Systems Using Standard Symbolic Regression

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

  author =       "Sebastien Gaucel and Maarten Keijzer and 
                 Evelyne Lutton and Alberto Tonda",
  title =        "Learning Dynamical Systems Using Standard Symbolic
  booktitle =    "17th European Conference on Genetic Programming",
  year =         "2014",
  editor =       "Miguel Nicolau and Krzysztof Krawiec and 
                 Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and 
                 Juan J. Merelo and Victor M. {Rivas Santos} and 
                 Kevin Sim",
  series =       "LNCS",
  volume =       "8599",
  publisher =    "Springer",
  pages =        "25--36",
  address =      "Granada, Spain",
  month =        "23-25 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-662-44302-6",
  DOI =          "doi:10.1007/978-3-662-44303-3_3",
  abstract =     "Symbolic regression has many successful applications
                 in learning free-form regular equations from data.
                 Trying to apply the same approach to differential
                 equations is the logical next step: so far, however,
                 results have not matched the quality obtained with
                 regular equations, mainly due to additional constraints
                 and dependencies between variables that make the
                 problem extremely hard to tackle. In this paper we
                 propose a new approach to dynamic systems learning.
                 Symbolic regression is used to obtain a set of
                 first-order Eulerian approximations of differential
                 equations, and mathematical properties of the
                 approximation are then exploited to reconstruct the
                 original differential equations. Advantages of this
                 technique include the de-coupling of systems of
                 differential equations, that can now be learnt
                 independently; the possibility of exploiting
                 established techniques for standard symbolic
                 regression, after trivial operations on the original
                 dataset; and the substantial reduction of computational
                 effort, when compared to existing ad-hoc solutions for
                 the same purpose. Experimental results show the
                 efficacy of the proposed approach on an instance of the
                 Lotka-Volterra model.",
  notes =        "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in
                 conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014
                 and EvoApplications2014",

Genetic Programming entries for Sebastien Gaucel Maarten Keijzer Evelyne Lutton Alberto Tonda