Estimating MLC NAND flash endurance: a genetic programming based symbolic regression application

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Damien Hogan and Tom Arbuckle and Conor Ryan",
  title =        "Estimating MLC NAND flash endurance: a genetic
                 programming based symbolic regression application",
  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 =        "1285--1292",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463537",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "NAND Flash memory is a multi-billion dollar industry
                 which is projected to continue to show significant
                 growth until at least 2017. Devices such as
                 smart-phones, tablets and Solid State Drives use NAND
                 Flash since it has numerous advantages over Hard Disk
                 Drives including better performance, lower power
                 consumption, and lower weight. However, storage
                 locations within Flash devices have a limited working
                 lifetime, as they slowly degrade through use,
                 eventually becoming unreliable and failing. The number
                 of times a location can be programmed is termed its
                 endurance, and can vary significantly, even between
                 locations within the same device. There is currently no
                 technique available to predict endurance, resulting in
                 manufacturers placing extremely conservative
                 specifications on their Flash devices. We perform
                 symbolic regression using Genetic Programming to
                 estimate the endurance of storage locations, based only
                 on the duration of program and erase operations
                 recorded from them. We show that the quality of
                 estimations for a device can be refined and improved as
                 the device continues to be used, and investigate a
                 number of different approaches to deal with the
                 significant variations in the endurance of storage
                 locations. Results show this technique's huge potential
                 for real-world application.",
  notes =        "Also known as \cite{2463537} 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 Damien Hogan Tom Arbuckle Conor Ryan