Knowledge interaction with genetic programming in mechatronic systems design using bond graphs

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

@Article{journals/tsmc/WangFTG05,
  title =        "Knowledge interaction with genetic programming in
                 mechatronic systems design using bond graphs",
  author =       "Jiachuan Wang and Zhun Fan and Janis P. Terpenny and 
                 Erik D. Goodman",
  journal =      "IEEE Transactions on Systems, Man, and Cybernetics,
                 Part C",
  year =         "2005",
  number =       "2",
  volume =       "35",
  pages =        "172--182",
  month =        may,
  bibdate =      "2006-01-23",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/tsmc/tsmcc35.html#WangFTG05",
  keywords =     "genetic algorithms, genetic programming, band-pass
                 filters, bond graphs, intelligent design assistants,
                 knowledge acquisition, mechanical engineering
                 computing, mechatronics, micromechanical devices, MEMS
                 bandpass filter design application, bond graphs,
                 evolutionary computation, knowledge discovery,
                 knowledge interaction, mechatronic systems design,
                 quarter-car suspension control system synthesis,
                 unified network synthesis approach, Bond graphs, MEMS
                 filter design, controller synthesis, knowledge
                 interaction, mechatronics",
  DOI =          "doi:10.1109/TSMCC.2004.841915",
  size =         "11 pages",
  abstract =     "This paper describes a unified network synthesis
                 approach for the conceptual stage of mechatronic
                 systems design using bond graphs. It facilitates
                 knowledge interaction with evolutionary computation
                 significantly by encoding the structure of a bond graph
                 in a genetic programming tree representation. On the
                 one hand, since bond graphs provide a succinct set of
                 basic design primitives for mechatronic systems
                 modelling, it is possible to extract useful modular
                 design knowledge discovered during the evolutionary
                 process for design creativity and reusability. On the
                 other hand, design knowledge gained from experience can
                 be incorporated into the evolutionary process to
                 improve the topologically open-ended search capability
                 of genetic programming for enhanced search efficiency
                 and design feasibility. This integrated knowledge-based
                 design approach is demonstrated in a quarter-car
                 suspension control system synthesis and a MEMS bandpass
                 filter design application.",
  notes =        "openbeagle",
}

Genetic Programming entries for Jiachuan Wang Zhun Fan Janis P Terpenny Erik Goodman

Citations