Cartesian Genetic Programming as Local Optimizer of Logic Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Sekanina:2014:CEC,
  title =        "Cartesian Genetic Programming as Local Optimizer of
                 Logic Networks",
  author =       "Lukas Sekanina and Ondrej Ptak and Zdenek Vasicek",
  pages =        "2901--2908",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
                 Computation",
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 genetic programming, Hardware Aspects of Bio-Inspired
                 Architectures and Systems (HABIAS)",
  DOI =          "doi:10.1109/CEC.2014.6900326",
  abstract =     "Logic synthesis and optimisation methods work either
                 globally on the whole logic network or locally on
                 preselected sub-networks. Evolutionary design methods
                 have already been applied to evolve and optimise logic
                 circuits at the global level. In this paper, we propose
                 a new method based on Cartesian genetic programming
                 (CGP) as a local area optimiser in combinational logic
                 networks. First, a subcircuit is extracted from a
                 complex circuit, then the subcircuit is optimised by
                 CGP and finally the optimised subcircuit replaces the
                 original one. The procedure is repeated until a
                 termination criterion is satisfied. We present a
                 performance comparison of local and global evolutionary
                 optimisation methods with a conventional approach based
                 on ABC and analyse these methods using differently
                 pre-optimised benchmark circuits. If a sufficient time
                 is available, the proposed locally optimising CGP gives
                 better results than other locally operating methods
                 reported in the literature; however, its performance is
                 significantly worse than the evolutionary global
                 optimisation.",
  notes =        "WCCI2014",
}

Genetic Programming entries for Lukas Sekanina Ondrej Ptak Zdenek Vasicek

Citations