Approximate Computing: An Old Job for Cartesian Genetic Programming?

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Lukas Sekanina",
  title =        "Approximate Computing: An Old Job for Cartesian
                 Genetic Programming?",
  booktitle =    "Inspired by Nature: Essays Presented to Julian F.
                 Miller on the Occasion of his 60th Birthday",
  publisher =    "Springer",
  year =         "2017",
  editor =       "Susan Stepney and Andrew Adamatzky",
  volume =       "28",
  series =       "Emergence, Complexity and Computation",
  chapter =      "9",
  pages =        "195--212",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming",
  isbn13 =       "978-3-319-67996-9",
  DOI =          "doi:10.1007/978-3-319-67997-6_9",
  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.",
  notes =        "part of \cite{miller60book}

Genetic Programming entries for Lukas Sekanina