Comparison of Robustness of Three Filter Design Strategies Using Genetic Programming and Bond Graphs

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Xiangdong Peng and Erik D. Goodman and 
                 Ronald C. Rosenberg",
  title =        "Comparison of Robustness of Three Filter Design
                 Strategies Using Genetic Programming and Bond Graphs",
  booktitle =    "Genetic Programming Theory and Practice {IV}",
  year =         "2006",
  editor =       "Rick L. Riolo and Terence Soule and Bill Worzel",
  volume =       "5",
  series =       "Genetic and Evolutionary Computation",
  pages =        "203--217",
  address =      "Ann Arbor",
  month =        "11-13 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, bond graph,
                 robust design strategy, Bessel analog filter design",
  ISBN =         "0-387-33375-4",
  DOI =          "doi:10.1007/978-0-387-49650-4_13",
  size =         "17 pages",
  abstract =     "A possible goal in robust design of dynamic systems is
                 to find a system topology under which the sensitivity
                 of performance to the values of component parameters is
                 minimised. This can provide robust performance in the
                 face of environmental change (resistance variation with
                 temperature, for example) and/or manufacturing-induced
                 variability in parameter values. In some cases, a
                 topology that is relatively insensitive to parameter
                 variation may allow use of less expensive (looser
                 tolerance) components. Cost of components, in some
                 instances, also depends on whether 'standard-sized'
                 components may be used or custom values are required.
                 This is true whether the components are electrical
                 components, mechanical fasteners, or hydraulic
                 fittings. However, using only standardsized or
                 preferred-value components introduces an additional
                 design constraint. This chapter uses genetic
                 programming to develop bond graphs specifying component
                 topology and parameter values for an example task,
                 designing a passive analog low pass filter with
                 fifth-order Bessel characteristics. It explores three
                 alternative design approaches. The first uses
                 'standard' GP and evolves designs in which components
                 can take on arbitrary values (i.e., custom design). The
                 second approach adds random noise to each parameter and
                 evaluates each design ten times; then, at the end of
                 the evolution, for the best design found, it 'snaps'
                 its parameter values to a small (component specific)
                 set of standard values. The third approach uses only
                 the small set of allowable standard values throughout
                 the evolutionary process, evaluating each design ten
                 times after addition of noise to each standard
                 parameter value. Then the best designs emerging from
                 each of these three procedures are compared for
                 robustness to parameter variation, evaluating each of
                 them one hundred times with random perturbations of
                 their parameters. Results indicated that, for this
                 preliminary study, the third method produced the most
                 robust designs, and the second method was better than
                 the first.",
  notes =        "part of \cite{Riolo:2006:GPTP} Published Jan 2007
                 after the workshop.",

Genetic Programming entries for Xiangdong Peng Erik Goodman Ronald C Rosenberg