Robust engineering design of electronic circuits with active components using genetic programming and bond Graphs

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

  author =       "Xiangdong Peng and Erik D. Goodman and 
                 Ronald C. Rosenberg",
  title =        "Robust engineering design of electronic circuits with
                 active components using genetic programming and bond
  booktitle =    "Genetic Programming Theory and Practice {V}",
  year =         "2007",
  editor =       "Rick L. Riolo and Terence Soule and Bill Worzel",
  series =       "Genetic and Evolutionary Computation",
  chapter =      "11",
  pages =        "187--202",
  address =      "Ann Arbor",
  month =        "17-19" # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-387-76308-8",
  DOI =          "doi:10.1007/978-0-387-76308-8_11",
  size =         "15 pages",
  abstract =     "Genetic programming has been used by Koza and many
                 others to design electrical, mechanical, and
                 mechatronic systems, including systems with both active
                 and passive components. This work has often required
                 large population sizes (on the order of ten thousand)
                 and millions of design evaluations to allow evolution
                 of both the topology and parameters of interesting
                 systems. For several years, the authors have studied
                 the evolution of multi-domain engineering systems
                 represented as bond graphs, a form that provides a
                 unified representation of mechanical, electrical,
                 hydraulic, pneumatic, thermal, and other systems in a
                 unified representation. Using this approach, called the
                 Genetic Programming/Bond Graph (GPBG) approach, they
                 have tried to evolve systems with perhaps tens of
                 components, but looking at only 100,000 or fewer design
                 candidates. The GPBG system uses much smaller
                 population sizes, but seeks to maintain diverse search
                 by using sustained evolutionary search processes such
                 as the Hierarchical Fair Competition principle and its
                 derivatives. It uses stochastic setting of parameter
                 values (resistances, capacitances, etc.) as a means of
                 evolving more robust designs. However, in past work,
                 the GPBG system was able to model and simulate only
                 passive components and simple (voltage or current, in
                 the case of electrical systems) sources, which severely
                 restricted the domain of problems it could address.
                 Thus, this paper reports the first steps in enhancing
                 the system to include active components. To date, only
                 three models of a transistor and one model of an
                 operational amplifier (op amp) are analysed and
                 implemented as two-port bond graph components. The
                 analysis method and design strategy can be easily
                 extended to other models or other active components or
                 even multi-port components. This chapter describes
                 design of an active analog low-pass filter with
                 fifth-order Bessel characteristics. A passive filter
                 with the same characteristics is also evolved with
                 GPBG. Then the best designs emerging from each of these
                 two procedures are compared. [The runs reported here
                 are intended only to document that the analysis tools
                 are working, and to begin study of the effects of
                 stochasticity, but not to determine the power of the
                 design procedure. The initial runs did not use HFC or
                 structure fitness sharing, which will be included as
                 soon as possible. Suitable problems will be tackled,
                 and results with suitable numbers of replicates to
                 allow drawing of statistically valid conclusions will
                 be reported in this paper, to determine whether
                 interesting circuits can be evolved more efficiently in
                 this framework than using other GP approaches.]",
  notes =        "part of \cite{Riolo:2007:GPTP} published 2008",

Genetic Programming entries for Xiangdong Peng Erik Goodman Ronald C Rosenberg