How Early and with How Little Data? Using Genetic Programing to Evolve Endurance Classifiers for MLC NAND Flash Memory

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

@InProceedings{hogan:2013:EuroGP,
  author =       "Damien Hogan and Tom Arbuckle and Conor Ryan",
  title =        "How Early and with How Little Data? Using Genetic
                 Programing to Evolve Endurance Classifiers for MLC NAND
                 Flash Memory",
  booktitle =    "Proceedings of the 16th European Conference on Genetic
                 Programming, EuroGP 2013",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and 
                 A. Sima Uyar and Bin Hu",
  series =       "LNCS",
  volume =       "7831",
  publisher =    "Springer Verlag",
  address =      "Vienna, Austria",
  pages =        "253--264",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Binary
                 Classifier, Flash Memory",
  isbn13 =       "978-3-642-37206-3",
  DOI =          "doi:10.1007/978-3-642-37207-0_22",
  abstract =     "Despite having a multi-billion dollar market and many
                 operational advantages, Flash memory suffers from a
                 serious drawback, that is, the gradual degradation of
                 its storage locations through use. Manufacturers
                 currently have no method to predict how long they will
                 function correctly, resulting in extremely conservative
                 longevity specifications being placed on Flash devices.
                 We leverage the fact that the durations of two crucial
                 Flash operations, program and erase, change as the
                 chips age. Their timings, recorded at intervals early
                 in chips' working lifetimes, are used to predict
                 whether storage locations will function correctly after
                 given numbers of operations. We examine how early and
                 with how little data such predictions can be made.
                 Genetic Programming, employing the timings as inputs,
                 is used to evolve binary classifiers that achieve up to
                 a mean of 97.88percent correct classification. This
                 technique displays huge potential for real-world
                 application, with resulting savings for
                 manufacturers.",
  notes =        "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in
                 conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013
                 and EvoApplications2013",
}

Genetic Programming entries for Damien Hogan Tom Arbuckle Conor Ryan

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