Gaining Deeper Insights in Symbolic Regression

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

@InCollection{Affenzeller:2013:GPTP,
  author =       "Michael Affenzeller and Stephan M. Winkler and 
                 Gabriel Kronberger and Michael Kommenda and Bogdan Burlacu and 
                 Stefan Wagner",
  title =        "Gaining Deeper Insights 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 =      "10",
  pages =        "175--190",
  address =      "Ann Arbor, USA",
  month =        "9-11 " # may,
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 regression, Algorithm analysis, Population diversity
                 Building block analysis, Genealogy, Variable networks",
  isbn13 =       "978-1-4939-0374-0",
  DOI =          "doi:10.1007/978-1-4939-0375-7_10",
  abstract =     "A distinguishing feature of symbolic regression using
                 genetic programming is its ability to identify complex
                 nonlinear white-box models. This is especially relevant
                 in practice where models are extensively scrutinised in
                 order to gain knowledge about underlying processes.
                 This potential is often diluted by the ambiguity and
                 complexity of the models produced by genetic
                 programming. In this contribution we discuss several
                 analysis methods with the common goal to enable better
                 insights in the symbolic regression process and to
                 produce models that are more understandable and show
                 better generalisation. In order to gain more
                 information about the process we monitor and analyse
                 the progresses of population diversity, building block
                 information, and even more general genealogy
                 information. Regarding the analysis of results, several
                 aspects such as model simplification, relevance of
                 variables, node impacts, and variable network analysis
                 are presented and discussed.",
  notes =        "http://cscs.umich.edu/gptp-workshops/

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

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

Citations