Symbolic Regression Is Not Enough: It Takes a Village to Raise a Model

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

@InCollection{Kotanchek:2012:GPTP,
  author =       "Mark E. Kotanchek and Ekaterina Vladislavleva and 
                 Guido Smits",
  title =        "Symbolic Regression Is Not Enough: It Takes a Village
                 to Raise a Model",
  booktitle =    "Genetic Programming Theory and Practice X",
  year =         "2012",
  series =       "Genetic and Evolutionary Computation",
  editor =       "Rick Riolo and Ekaterina Vladislavleva and 
                 Marylyn D. Ritchie and Jason H. Moore",
  publisher =    "Springer",
  chapter =      "13",
  pages =        "187--203",
  address =      "Ann Arbor, USA",
  month =        "12-14 " # may,
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 regression, Post-processing, Model selection, Variable
                 selection, Evolvability",
  isbn13 =       "978-1-4614-6845-5",
  URL =          "http://dx.doi.org/10.1007/978-1-4614-6846-2_13",
  DOI =          "doi:10.1007/978-1-4614-6846-2_13",
  abstract =     "From a real-world perspective, good enough has been
                 achieved in the core representations and evolutionary
                 strategies of genetic programming assuming
                 state-of-the-art algorithms and implementations are
                 being used. What is needed for industrial symbolic
                 regression are tools to (a) explore and refine the
                 data, (b) explore the developed model space and extract
                 insight and guidance from the available sample of the
                 infinite possibilities of model forms and (c) identify
                 appropriate models for deployment as predictors,
                 emulators, etc. This chapter focuses on the approaches
                 used in DataModeler to address the modelling life
                 cycle. A special focus in this chapter is the
                 identification of driving variables and meta variables.
                 Exploiting the diversity of search paths followed
                 during independent evolutions and, then, looking at the
                 distributions of variables and metavariable usage also
                 provides an opportunity to gather key insights. The
                 goal in this framework, however, is not to replace the
                 modeller but, rather, to augment the inclusion of
                 context and collection of insight by removing
                 mechanistic requirements and facilitating the ability
                 to think. We believe that the net result is higher
                 quality and more robust models.",
  notes =        "part of \cite{Riolo:2012:GPTP} published after the
                 workshop in 2013",
}

Genetic Programming entries for Mark Kotanchek Ekaterina (Katya) Vladislavleva Guido F Smits

Citations