Approximate Computing: An Old Job for Cartesian Genetic Programming?

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InCollection{Sekanina:2017:miller,

• 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} https://link.springer.com/bookseries/10624",

}

Genetic Programming entries for Lukas Sekanina

Citations