On size, complexity and generalisation error in GP

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

@InProceedings{Fitzgerald:2014:GECCO,
  author =       "Jeannie Fitzgerald and Conor Ryan",
  title =        "On size, complexity and generalisation error in GP",
  booktitle =    "GECCO '14: Proceedings of the 2014 conference on
                 Genetic and evolutionary computation",
  year =         "2014",
  editor =       "Christian Igel and Dirk V. Arnold and 
                 Christian Gagne and Elena Popovici and Anne Auger and 
                 Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and 
                 Kalyanmoy Deb and Benjamin Doerr and James Foster and 
                 Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and 
                 Hitoshi Iba and Christian Jacob and Thomas Jansen and 
                 Yaochu Jin and Marouane Kessentini and 
                 Joshua D. Knowles and William B. Langdon and Pedro Larranaga and 
                 Sean Luke and Gabriel Luque and John A. W. McCall and 
                 Marco A. {Montes de Oca} and Alison Motsinger-Reif and 
                 Yew Soon Ong and Michael Palmer and 
                 Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and 
                 Guenther Ruhe and Tom Schaul and Thomas Schmickl and 
                 Bernhard Sendhoff and Kenneth O. Stanley and 
                 Thomas Stuetzle and Dirk Thierens and Julian Togelius and 
                 Carsten Witt and Christine Zarges",
  isbn13 =       "978-1-4503-2662-9",
  pages =        "903--910",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2576768.2598346",
  DOI =          "doi:10.1145/2576768.2598346",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "For some time, Genetic Programming research has lagged
                 behind the wider Machine Learning community in the
                 study of generalisation, where the decomposition of
                 generalisation error into bias and variance components
                 is well understood. However, recent Genetic Programming
                 contributions focusing on complexity, size and bloat as
                 they relate to over-fitting have opened up some
                 interesting avenues of research. In this paper, we
                 carry out a simple empirical study on five binary
                 classification problems. The study is designed to
                 discover what effects may be observed when program size
                 and complexity are varied in combination, with the
                 objective of gaining a better understanding of
                 relationships which may exist between solution size,
                 operator complexity and variance error. The results of
                 the study indicate that the simplest configuration, in
                 terms of operator complexity, consistently results in
                 the best average performance, and in many cases, the
                 result is significantly better. We further demonstrate
                 that the best results are achieved when this minimum
                 complexity set-up is combined with a less than
                 parsimonious permissible size.",
  notes =        "Also known as \cite{2598346} GECCO-2014 A joint
                 meeting of the twenty third international conference on
                 genetic algorithms (ICGA-2014) and the nineteenth
                 annual genetic programming conference (GP-2014)",
}

Genetic Programming entries for Jeannie Fitzgerald Conor Ryan

Citations