Approximate Computing: An Old Job for Cartesian Genetic Programming?
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InCollection{Sekanina:2017:miller,
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author = "Lukas Sekanina",
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title = "Approximate Computing: An Old Job for Cartesian
Genetic Programming?",
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booktitle = "Inspired by Nature: Essays Presented to Julian F.
Miller on the Occasion of his 60th Birthday",
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publisher = "Springer",
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year = "2017",
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editor = "Susan Stepney and Andrew Adamatzky",
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volume = "28",
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series = "Emergence, Complexity and Computation",
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chapter = "9",
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pages = "195--212",
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming",
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isbn13 = "978-3-319-67996-9",
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DOI = "doi:10.1007/978-3-319-67997-6_9",
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abstract = "Miller's Cartesian genetic programming (CGP) has
significantly influenced the development of
evolutionary circuit design and evolvable hardware. We
present key ingredients of CGP with respect to the
efficient search in the space of digital circuits. We
then show that approximate computing, which is
currently one of the promising approaches used to
reduce power consumption of computer systems, is a
natural application for CGP. We briefly survey typical
applications of CGP in approximate circuit design and
outline new directions in approximate computing that
could benefit from CGP.",
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notes = "part of \cite{miller60book}
https://link.springer.com/bookseries/10624",
- }
Genetic Programming entries for
Lukas Sekanina
Citations