Symbolic regression of multiple-time-scale dynamical systems

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

@InProceedings{Cornforth:2012:GECCO,
  author =       "Theodore Cornforth and Hod Lipson",
  title =        "Symbolic regression of multiple-time-scale dynamical
                 systems",
  booktitle =    "GECCO '12: Proceedings of the fourteenth international
                 conference on Genetic and evolutionary computation
                 conference",
  year =         "2012",
  editor =       "Terry Soule and Anne Auger and Jason Moore and 
                 David Pelta and Christine Solnon and Mike Preuss and 
                 Alan Dorin and Yew-Soon Ong and Christian Blum and 
                 Dario Landa Silva and Frank Neumann and Tina Yu and 
                 Aniko Ekart and Will Browne and Tim Kovacs and 
                 Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and 
                 Giovanni Squillero and Nicolas Bredeche and 
                 Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and 
                 Martin Pelikan and Silja Meyer-Nienberg and 
                 Christian Igel and Greg Hornby and Rene Doursat and 
                 Steve Gustafson and Gustavo Olague and Shin Yoo and 
                 John Clark and Gabriela Ochoa and Gisele Pappa and 
                 Fernando Lobo and Daniel Tauritz and Jurgen Branke and 
                 Kalyanmoy Deb",
  isbn13 =       "978-1-4503-1177-9",
  pages =        "735--742",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330163.2330266",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Genetic programming has been successfully used for
                 symbolic regression of time series data in a wide
                 variety of applications. However, previous approaches
                 have not taken into account the presence of
                 multiple-time-scale dynamics despite their prevalence
                 in both natural and artificial dynamical systems. Here,
                 we propose an algorithm that first decomposes data from
                 such systems into components with dynamics at different
                 time scales and then performs symbolic regression
                 separately for each scale. Results show that this
                 divide-and-conquer approach improves the accuracy and
                 efficiency with which genetic programming can be used
                 to reverse-engineer multiple-time-scale dynamical
                 systems.",
  notes =        "Also known as \cite{2330266} GECCO-2012 A joint
                 meeting of the twenty first international conference on
                 genetic algorithms (ICGA-2012) and the seventeenth
                 annual genetic programming conference (GP-2012)",
}

Genetic Programming entries for Theodore W Cornforth Hod Lipson

Citations