Enhancing regression models for complex systems using evolutionary techniques for feature engineering

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

@Article{Arroba:2015:grid,
  title =        "Enhancing regression models for complex systems using
                 evolutionary techniques for feature engineering",
  author =       "Patricia Arroba and Jose Luis Risco-Martin and 
                 Marina Zapater and Jose Manuel Moya and Jose Luis Ayala",
  journal =      "Journal of Grid Computing",
  year =         "2015",
  volume =       "13",
  number =       "3",
  pages =        "409--423",
  publisher =    "Springer",
  month =        sep # "~27",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://eprints.ucm.es/30960/",
  URL =          "http://eprints.ucm.es/30960/1/JGridComputing2014.pdf",
  URL =          "http://link.springer.com/article/10.1007%2Fs10723-014-9313-8",
  ISSN =         "1572-9184",
  DOI =          "doi:10.1007/s10723-014-9313-8",
  abstract =     "This work proposes an automatic methodology for
                 modelling complex systems. Our methodology is based on
                 the combination of Grammatical Evolution and classical
                 regression to obtain an optimal set of features that
                 take part of a linear and convex model. This technique
                 provides both Feature Engineering and Symbolic
                 Regression in order to infer accurate models with no
                 effort or designer's expertise requirements. As
                 advanced Cloud services are becoming mainstream, the
                 contribution of data centers in the overall power
                 consumption of modern cities is growing dramatically.
                 These facilities consume from 10 to 100 times more
                 power per square foot than typical office buildings.
                 Modeling the power consumption for these
                 infrastructures is crucial to anticipate the effects of
                 aggressive optimisation policies, but accurate and fast
                 power modelling is a complex challenge for high-end
                 servers not yet satisfied by analytical approaches. For
                 this case study, our methodology minimises error in
                 power prediction. This work has been tested using real
                 Cloud applications resulting on an average error in
                 power estimation of 3.98percent. Our work improves the
                 possibilities of deriving Cloud energy efficient
                 policies in Cloud data centers being applicable to
                 other computing environments with similar
                 characteristics.",
  bibsource =    "OAI-PMH server at eprints.ucm.es",
  language =     "en",
  oai =          "oai:www.ucm.es:30960",
  relation =     "10.1007/s10723-014-9313-8; TIN2008-00508",
  rights =       "info:eu-repo/semantics/openAccess",
  type =         "PeerReviewed",
}

Genetic Programming entries for Patricia Arroba Jose L Risco-Martin Marina Zapater Jose Manuel Moya Jose Luis Ayala

Citations