A Deterministic and Symbolic Regression Hybrid Applied to Resting-State fMRI Data

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

@InCollection{Icke:2013:GPTP,
  author =       "Ilknur Icke and Nicholas A. Allgaier and 
                 Christopher M. Danforth and Robert A. Whelan and 
                 Hugh P. Garavan and Joshua C. Bongard",
  title =        "A Deterministic and Symbolic Regression Hybrid Applied
                 to Resting-State {fMRI} Data",
  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 =      "9",
  pages =        "155--173",
  address =      "Ann Arbor, USA",
  month =        "9-11 " # may,
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 regression, Hybrid algorithm, Regularisation,
                 Resting-state fMRI",
  isbn13 =       "978-1-4939-0374-0",
  oai =          "oai:CiteSeerX.psu:10.1.1.368.634",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.368.634",
  URL =          "http://www.cs.uvm.edu/~jbongard/papers/2013_GPTP_Icke.pdf",
  DOI =          "doi:10.1007/978-1-4939-0375-7_9",
  abstract =     "Symbolic regression (SR) is one the most popular
                 applications of genetic programming (GP) and an
                 attractive alternative to the standard deterministic
                 regression approaches due to its flexibility in
                 generating free-form mathematical models from observed
                 data without any domain knowledge. However, GP suffers
                 from various issues hindering the applicability of the
                 technique to real-life problems. In this paper, we show
                 that a hybrid deterministic regression (DR)/genetic
                 programming based symbolic regression (GP-SR) algorithm
                 outperforms GP-SR alone on a brain imaging dataset.",
  notes =        "http://cscs.umich.edu/gptp-workshops/

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

Genetic Programming entries for Ilknur Icke Nicholas A Allgaier Christopher M Danforth Robert A Whelan Hugh P Garavan Josh C Bongard

Citations