Cartesian GP in Optimization of Combinational Circuits with Hundreds of Inputs and Thousands of Gates

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

@InProceedings{Vasicek:2015:EuroGPa,
  author =       "Zdenek Vasicek",
  title =        "{Cartesian GP} in Optimization of Combinational
                 Circuits with Hundreds of Inputs and Thousands of
                 Gates",
  booktitle =    "18th European Conference on Genetic Programming",
  year =         "2015",
  editor =       "Penousal Machado and Malcolm I. Heywood and 
                 James McDermott and Mauro Castelli and 
                 Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
  series =       "LNCS",
  volume =       "9025",
  publisher =    "Springer",
  pages =        "139--150",
  address =      "Copenhagen",
  month =        "8-10 " # apr,
  organisation = "EvoStar",
  note =         "best paper award at EuroGP 2015",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 genetic programming, Evolutionary optimization,
                 Combinational circuits, Formal verification",
  isbn13 =       "978-3-319-16500-4",
  DOI =          "doi:10.1007/978-3-319-16501-1_12",
  abstract =     "A new approach to the evolutionary optimization of
                 large digital circuits is introduced in this paper. In
                 contrast with evolutionary circuit design, the goal of
                 the evolutionary circuit optimization is to minimize
                 the number of gates (or other non-functional
                 parameters) of already functional circuit. The method
                 combines a circuit simulation with a formal
                 verification in order to detect the functional
                 inequivalence of the parent and its offspring. An
                 extensive set of 100 benchmarks circuits is used to
                 evaluate the performance of the method as well as the
                 evolutionary approach. Moreover, the role of neutral
                 mutations in the context of evolutionary optimization
                 is investigated. In average, the method enabled a
                 34percent reduction in gate count even if the optimizer
                 was executed only for 15 minutes",
  notes =        "Part of \cite{Machado:2015:GP} EuroGP'2015 held in
                 conjunction with EvoCOP2015, EvoMusArt2015 and
                 EvoApplications2015",
}

Genetic Programming entries for Zdenek Vasicek

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