Learning regression ensembles with genetic programming at scale

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Kalyan Veeramachaneni and Owen Derby and 
                 Dylan Sherry and Una-May O'Reilly",
  title =        "Learning regression ensembles with genetic programming
                 at scale",
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and Anne Auger and 
                 Jaume Bacardit and Josh Bongard and Juergen Branke and 
                 Nicolas Bredeche and Dimo Brockhoff and 
                 Francisco Chicano and Alan Dorin and Rene Doursat and 
                 Aniko Ekart and Tobias Friedrich and Mario Giacobini and 
                 Mark Harman and Hitoshi Iba and Christian Igel and 
                 Thomas Jansen and Tim Kovacs and Taras Kowaliw and 
                 Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and 
                 John McCall and Alberto Moraglio and 
                 Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and 
                 Gustavo Olague and Yew-Soon Ong and 
                 Michael E. Palmer and Gisele Lobo Pappa and 
                 Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and 
                 Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and 
                 Daniel Tauritz and Leonardo Vanneschi",
  isbn13 =       "978-1-4503-1963-8",
  pages =        "1117--1124",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463506",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In this paper we examine the challenge of producing
                 ensembles of regression models for large datasets. We
                 generate numerous regression models by concurrently
                 executing multiple independent instances of a genetic
                 programming learner. Each instance may be configured
                 with different parameters and a different subset of the
                 training data. Several strategies for fusing
                 predictions from multiple regression models are
                 compared. To overcome the small memory size of each
                 instance, we challenge our framework to learn from
                 small subsets of training data and yet produce a
                 prediction of competitive quality after fusion. This
                 decreases the running time of learning which produces
                 models of good quality in a timely fashion. Finally, we
                 examine the quality of fused predictions over the
                 progress of the computation.",
  notes =        "Also known as \cite{2463506} GECCO-2013 A joint
                 meeting of the twenty second international conference
                 on genetic algorithms (ICGA-2013) and the eighteenth
                 annual genetic programming conference (GP-2013)",

Genetic Programming entries for Kalyan Veeramachaneni Owen C Derby Dylan Sherry Una-May O'Reilly