CAFFEINE: Template-Free Symbolic Model Generation of Analog Circuits via Canonical Form Functions and Genetic Programming

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@InProceedings{mcconaghy:2005:DATE,
  author =       "Trent McConaghy and Tom Eeckelaert and 
                 Georges Gielen",
  title =        "CAFFEINE: Template-Free Symbolic Model Generation of
                 Analog Circuits via Canonical Form Functions and
                 Genetic Programming",
  booktitle =    "Proceedings of the Design Automation and Test Europe
                 (DATE) Conference",
  year =         "2005",
  pages =        "1082--1087",
  keywords =     "genetic algorithms, genetic programming",
  volume =       "2",
  address =      "Munich",
  organisation = "European Design and Automation Association, the EDA
                 Consortium, the IEEE Computer Society - TTTC, ECSI, RAS
                 and ACM SIGDA",
  ISSN =         "1530-1591",
  URL =          "http://arxiv.org/abs/0710.4630",
  DOI =          "doi:10.1109/DATE.2005.89",
  abstract =     "automatically generate compact symbolic performance
                 models of analog circuits with no prior specification
                 of an equation template. The approach takes SPICE
                 simulation data as input, which enables modeling of any
                 nonlinear circuits and circuit characteristics. Genetic
                 programming is applied as a means of traversing the
                 space of possible symbolic expressions. A grammar is
                 specially designed to constrain the search to a
                 canonical form for functions. Novel evolutionary search
                 operators are designed to exploit the structure of the
                 grammar. The approach generates a set of symbolic
                 models which collectively provide a tradeoff between
                 error and model complexity. Experimental results show
                 that the symbolic models generated are compact and easy
                 to understand, making this an effective method for
                 aiding understanding in analog design. The models also
                 demonstrate better prediction quality than
                 posynomials.",
  notes =        "http://www.date-conference.com/cgi-bin/prog05/show_conf_details.cgi?date=Thu
                 paper id? 230, KU Leuven, BE",
}

Genetic Programming entries for Trent McConaghy Tom Eeckelaert Georges G E Gielen

Citations