Parallel Multi-Objective Evolutionary Design of Approximate Circuits

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@InProceedings{Hrbacek:2015:GECCO,
  author =       "Radek Hrbacek",
  title =        "Parallel Multi-Objective Evolutionary Design of
                 Approximate Circuits",
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "687--694",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, Evolutionary Multiobjective
                 Optimization",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739480.2754785",
  DOI =          "doi:10.1145/2739480.2754785",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Evolutionary design of digital circuits has been well
                 established in recent years. Besides correct
                 functionality, the demands placed on current circuits
                 include the area of the circuit and its power
                 consumption. By relaxing the functionality requirement,
                 one can obtain more efficient circuits in terms of the
                 area or power consumption at the cost of an error
                 introduced to the output of the circuit. As a result, a
                 variety of trade-offs between error and efficiency can
                 be found. In this paper, a multi-objective evolutionary
                 algorithm for the design of approximate digital
                 circuits is proposed. The scalability of the
                 evolutionary design has been recently improved using
                 parallel implementation of the fitness function and by
                 employing spatially structured evolutionary algorithms.
                 The proposed multi-objective approach uses Cartesian
                 Genetic Programming for the circuit representation and
                 a modified NSGA-II algorithm. Multiple isolated islands
                 are evolving in parallel and the populations are
                 periodically merged and new populations are distributed
                 across the islands. The method is evaluated in the task
                 of approximate arithmetical circuits design.",
  notes =        "Also known as \cite{2754785} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",
}

Genetic Programming entries for Radek Hrbacek

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