Sliding Window Symbolic Regression for Detecting Changes of System Dynamics

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

@InProceedings{Winkler:2014:GPTP,
  author =       "Stephan M. Winkler and Michael Affenzeller and 
                 Gabriel Kronberger and Michael Kommenda and Bogdan Burlacu and 
                 Stefan Wagner",
  title =        "Sliding Window Symbolic Regression for Detecting
                 Changes of System Dynamics",
  booktitle =    "Genetic Programming Theory and Practice XII",
  year =         "2014",
  editor =       "Rick Riolo and William P. Worzel and Mark Kotanchek",
  series =       "Genetic and Evolutionary Computation",
  pages =        "91--107",
  address =      "Ann Arbor, USA",
  month =        "8-10 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 regression, Self-adaptive sliding window techniques,
                 System analysis, System dynamics",
  isbn13 =       "978-3-319-16029-0",
  DOI =          "doi:10.1007/978-3-319-16030-6_6",
  abstract =     "In this chapter we discuss sliding window symbolic
                 regression and its ability to systematically detect
                 changing dynamics in data streams. The sliding window
                 defines the portion of the data visible to the
                 algorithm during training and is moved over the data.
                 The window is moved regularly based on the generations
                 or on the current selection pressure when using
                 offspring selection. The sliding window technique has
                 the effect that population has to adapt to the
                 constantly changing environmental conditions.

                 In the empirical section of this chapter, we focus on
                 detecting change points of analysed systems' dynamics.
                 We show its effectiveness on various artificial data
                 sets and discuss the results obtained when the sliding
                 window moved in each generation and when it is moved
                 only when a selection pressure threshold is reached.
                 The results show that sliding window symbolic
                 regression can be used to detect change points in
                 systems dynamics for the considered data sets.",
  notes =        "http://cscs.umich.edu/gptp-workshops/

                 Part of \cite{Riolo:2014:GPTP} published after the
                 workshop in 2015",
}

Genetic Programming entries for Stephan M Winkler Michael Affenzeller Gabriel Kronberger Michael Kommenda Bogdan Burlacu Stefan Wagner

Citations