Genetic Improvement for Approximate Computing
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gp-bibliography.bib Revision:1.7964
- @InProceedings{Sekanina:2016:WAPCO,
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author = "Lukas Sekanina and Zdenek Vasicek",
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title = "Genetic Improvement for Approximate Computing",
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booktitle = "2nd Workshop On Approximate Computing (WAPCO 2016)",
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year = "2016",
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editor = "George Karakonstantis and Costas Bekas and
Dimitris Gizopoulos and Nikolaos Bellas",
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address = "Prague",
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month = jan # " 20",
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keywords = "genetic algorithms, genetic programming, Genetic
Improvement, median",
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URL = "http://wapco.inf.uth.gr/papers/SESSION2/wapco2016_2_5.pdf",
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size = "2 pages",
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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.",
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notes = "gismo http://wapco.inf.uth.gr/index.html In
conjunction with HiPEAC 2016",
- }
Genetic Programming entries for
Lukas Sekanina
Zdenek Vasicek
Citations