Evolutionary Approximation of Complex Digital Circuits

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Zdenek Vasicek and Lukas Sekanina",
  title =        "Evolutionary Approximation of Complex Digital
  booktitle =    "GECCO Companion '15: Proceedings of the Companion
                 Publication 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 =       "978-1-4503-3488-4",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming: Poster",
  pages =        "1505--1506",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739482.2764657",
  DOI =          "doi:10.1145/2739482.2764657",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Circuit approximation has been developed in recent
                 years as a viable method for constructing energy
                 efficient electronic systems. An open problem is how to
                 effectively obtain approximate circuits showing good
                 compromises between key circuit parameters -- the
                 error, power consumption, area and delay. The use of
                 evolutionary algorithms in the task of circuit
                 approximation has led to promising results; however,
                 only relative simple circuit instances have been
                 tackled because of the scalability problems of the
                 evolutionary design method. We propose to replace the
                 most time consuming part of the evolutionary design
                 algorithm, i.e. the fitness calculation exponentially
                 depending on the number of circuit inputs, by an
                 equivalence checking algorithm operating over Binary
                 Decision Diagrams (BDDs). Approximate circuits are
                 evolved using Cartesian genetic programming which calls
                 a BDD solver to calculate the fitness value of
                 candidate circuits. The method enables to obtain
                 approximate circuits consisting of tens of inputs and
                 hundreds of gates and showing desired trade-off between
                 key circuit parameters.",
  notes =        "Also known as \cite{2764657} Distributed at

Genetic Programming entries for Zdenek Vasicek Lukas Sekanina