Evolutionary Design of Both Topologies and Parameters of a Hybrid Dynamical System

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Jean-Francois Dupuis and Zhun Fan and 
                 Erik D. Goodman",
  title =        "Evolutionary Design of Both Topologies and Parameters
                 of a Hybrid Dynamical System",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2012",
  volume =       "16",
  number =       "3",
  pages =        "391--405",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Embryo,
                 Encoding, Junctions, Mechatronics, Switches, Automated
                 design, bond graphs, evolutionary design, hybrid
                 mechatronic systems",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2011.2159724",
  size =         "15 pages",
  abstract =     "This paper investigates the issue of evolutionary
                 design of open-ended plants for hybrid dynamical
                 systems, i.e., both their topologies and parameters.
                 Hybrid bond graphs (HBGs) are used to represent
                 dynamical systems involving both continuous and
                 discrete system dynamics. Genetic programming, with
                 some special mechanisms incorporated, is used as a
                 search tool to explore the open-ended design space of
                 hybrid bond graphs. Combination of these two tools,
                 i.e., HBGs and genetic programming, leads to an
                 approach called HBGGP that can automatically generate
                 viable design candidates of hybrid dynamical systems
                 that fulfill predefined design specifications. A
                 comprehensive investigation of a case study of DC-DC
                 converter design demonstrates the feasibility and
                 effectiveness of the HBGGP approach. Important
                 characteristics of the approach are also discussed,
                 with some future research directions pointed out.",
  notes =        "also known as \cite{6045329}",

Genetic Programming entries for Jean-Francois Dupuis Zhun Fan Erik Goodman