First Steps toward Automated Design of Mechatronic Systems Using Bond Graphs and Genetic Programming

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

@InProceedings{kisungseo:2001:gecco,
  title =        "First Steps toward Automated Design of Mechatronic
                 Systems Using Bond Graphs and Genetic Programming",
  author =       "Kisung Seo and Erik D. Goodman and 
                 Ronald C. Rosenberg",
  pages =        "189",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and 
                 W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and 
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and 
                 Max H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster, bond
                 graphs, dynamic systems design, mechatronic, systems
                 design",
  ISBN =         "1-55860-774-9",
  URL =          "http://garage.cse.msu.edu/papers/GARAGe01-07-03.pdf",
  URL =          "http://citeseer.ist.psu.edu/445817.html",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf",
  size =         "1 page",
  abstract =     "This paper suggests a method for automatically
                 synthesizing designs for mechatronic systems. The
                 domain of mechatronic systems includes mixtures of, for
                 example, electrical, mechanical, hydraulic, pneumatic,
                 and thermal components, making it difficult to design a
                 system to meet specified performance goals with a
                 single design tool. Bond graphs are domain independent,
                 allow free composition, and are efficient for
                 classification and analysis of models, allowing rapid
                 determination of various types of acceptability or
                 feasibility of candidate designs (Karnopp et al). This
                 can sharply reduce the time needed for analysis of
                 designs that are infeasible or otherwise unattractive.
                 Genetic programming is well recognized as a powerful
                 tool for open-ended search (Koza et al). The
                 combination of these two powerful methods is therefore
                 an appropriate target for a better system for synthesis
                 of complex multi-domain systems. The approach described
                 here will evolve new designs (represented as bond
                 graphs) with ever-improving performance, in an
                 iterative loop of synthesis, analysis, and feedback to
                 the synthesis process.",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of \cite{spector:2001:GECCO}",
}

Genetic Programming entries for Kisung Seo Erik Goodman Ronald C Rosenberg

Citations