Evolving Simple Symbolic Regression Models by Multi-objective Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Michael Kommenda and Gabriel Kronberger and 
                 Michael Affenzeller and Stephan Winkler and Bogdan Burlacu",
  title =        "Evolving Simple Symbolic Regression Models by
                 Multi-objective Genetic Programming",
  booktitle =    "Genetic Programming Theory and Practice XIII",
  year =         "2015",
  editor =       "Rick Riolo and William P. Worzel and M. Kotanchek and 
                 A. Kordon",
  series =       "Genetic and Evolutionary Computation",
  pages =        "1--19",
  address =      "Ann Arbor, USA",
  month =        "14-16 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 regression, Complexity measures, Multi-objective
                 optimization, NSGA-II",
  isbn13 =       "978-3-319-34223-8",
  URL =          "http://www.springer.com/us/book/9783319342214",
  DOI =          "doi:10.1007/978-3-319-34223-8_1",
  abstract =     "In this chapter we examine how multi-objective genetic
                 programming can be used to perform symbolic regression
                 and compare its performance to single-objective genetic
                 programming. Multi-objective optimization is
                 implemented by using a slightly adapted version of
                 NSGA-II, where the optimization objectives are the
                 model's prediction accuracy and its complexity. As the
                 model complexity is explicitly defined as an objective,
                 the evolved symbolic regression models are simpler and
                 more parsimonious when compared to models generated by
                 a single-objective algorithm. Furthermore, we define a
                 new complexity measure that includes syntactical and
                 semantic information about the model, while still being
                 efficiently computed, and demonstrate its performance
                 on several benchmark problems. As a result of the
                 multi-objective approach the appropriate model length
                 and the functions included in the models are
                 automatically determined without the necessity to
                 specify them a-priori.",
  notes =        "http://cscs.umich.edu/gptp-workshops/

                 Part of \cite{Riolo:2015:GPTP} Published after the
                 workshop in 2016",

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