Topological search in automated mechatronic system synthesis using bond graphs and genetic programming

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

@InProceedings{jianjunHu:2004:ACC,
  author =       "Jianjun Hu and Erik Goodman and Ronald Rosenberg",
  title =        "Topological search in automated mechatronic system
                 synthesis using bond graphs and genetic programming",
  booktitle =    "Proceedings of American Control Conference ACC 2004",
  year =         "2004",
  volume =       "6",
  pages =        "5628--5634",
  month =        jun # " 30-" # jul # " 2",
  organisation = "American Control Conference",
  address =      "Boston, MA, USA",
  email =        "hujianju@msu.edu",
  keywords =     "genetic algorithms, genetic programming, bond graphs,
                 control system synthesis, eigenvalues and
                 eigenfunctions, inverse problems, mechatronics, search
                 problems, automated mechatronic system synthesis, bond
                 graphs, eigenvalue placement problem, encoding, inverse
                 problem, open ended topology search, population
                 seeding, scalable benchmark problem",
  ISBN =         "0-7803-8335-4",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1384751",
  abstract =     "We have introduced a well-defined scalable benchmark
                 problem - the eigenvalue placement problem - to
                 investigate scalability issues in automated topology
                 synthesis of mechatronic systems based on bond graphs
                 and genetic programming. This classical inverse problem
                 shares characteristics with many other system synthesis
                 problems, such as electric circuit and controller
                 synthesis, in terms of epistasis and multi-modality of
                 the search space. Critical issues of open-ended
                 topology search by genetic programming are
                 investigated, including encoding, population seeding,
                 scalability and evolvability. For the eigenvalue
                 problems, we have found there exists a correlation
                 between structure and function that is important for
                 efficient topology search. Standard genetic programming
                 has been used to solve up to 20-eigen-value problems,
                 finding the target system of bush topology out of
                 823,065 possibilities with only 29506 topology
                 evaluations.",
  notes =        "Also known as \cite{1384751}",
}

Genetic Programming entries for Jianjun Hu Erik Goodman Ronald C Rosenberg

Citations