Towards compositional coevolution in evolutionary circuit design

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Sikulova:2014:ICES,
  author =       "M. Sikulova and G. Komjathy and L. Sekanina",
  booktitle =    "IEEE International Conference on Evolvable Systems
                 (ICES 2014)",
  title =        "Towards compositional coevolution in evolutionary
                 circuit design",
  year =         "2014",
  month =        dec,
  pages =        "157--164",
  abstract =     "A divide and conquer approach is one of the methods
                 introduced to get over the scalability problem of the
                 evolutionary circuit design. A complex circuit is
                 decomposed into modules which are evolved separately
                 and without any interaction. The benefits are in
                 reducing the search space and accelerating the
                 evaluation of candidate circuits. In this paper, the
                 evolution of non-interacting modules is replaced by a
                 coevolutionary algorithm, in which the fitness of a
                 module depends on fitness values of other modules, i.e.
                 the modules are adapted to work together. The proposed
                 method is embedded into Cartesian genetic programming
                 (CGP). The coevolutionary approach was evaluated in the
                 design of a switching image filter which was decomposed
                 into the filtering module and detector module. The
                 filters evolved using the proposed coevolutionary
                 method show a higher quality of filtering in comparison
                 with filters using independently evolved modules.
                 Furthermore, the whole design process was accelerated
                 1.31 times in comparison with the standard CGP.",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming",
  DOI =          "doi:10.1109/ICES.2014.7008735",
  notes =        "Also known as \cite{7008735}",
}

Genetic Programming entries for Michaela Sikulova G Komjathy Lukas Sekanina

Citations