Genetic Improvement for Approximate Computing

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@InProceedings{Sekanina:2016:WAPCO,
  author =       "Lukas Sekanina and Zdenek Vasicek",
  title =        "Genetic Improvement for Approximate Computing",
  booktitle =    "2nd Workshop On Approximate Computing (WAPCO 2016)",
  year =         "2016",
  editor =       "George Karakonstantis and Costas Bekas and 
                 Dimitris Gizopoulos and Nikolaos Bellas",
  address =      "Prague",
  month =        jan # " 20",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Improvement, median",
  URL =          "http://wapco.inf.uth.gr/papers/SESSION2/wapco2016_2_5.pdf",
  size =         "2 pages",
  abstract =     "This paper connects the Genetic Improvement (GI)
                 method, recently established in the search-based
                 software engineering community, with approximate
                 computing, in order to obtain improvements in the cases
                 when errors in computations can be tolerated. It is
                 argued that Genetic Improvement which shares many
                 objectives with the approximate computing can easily be
                 adopted to solve typical problems in the area of
                 approximate computing. An open problem is whether
                 GI-based methodology can really be accepted by the
                 approximate computing community.",
  notes =        "gismo http://wapco.inf.uth.gr/index.html In
                 conjunction with HiPEAC 2016",
}

Genetic Programming entries for Lukas Sekanina Zdenek Vasicek

Citations