Template-Free Symbolic Performance Modeling of Analog Circuits via Canonical-Form Functions and Genetic Programming

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@Article{McConaghy:2009:ieeeCADICS,
  author =       "Trent McConaghy and Georges G. E. Gielen",
  title =        "Template-Free Symbolic Performance Modeling of Analog
                 Circuits via Canonical-Form Functions and Genetic
                 Programming",
  journal =      "IEEE Transactions on Computer-Aided Design of
                 Integrated Circuits and Systems",
  year =         "2009",
  volume =       "28",
  pages =        "1162--1175",
  number =       "8",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, SPICE,
                 analogue circuits CAFFEINE, SPICE simulation data,
                 analog circuits, arbitrary nonlinear circuits,
                 canonical-form functions, compact interpretable
                 symbolic performance models, kriging, neural networks,
                 posynomials, product-of-sum layers, splines,
                 sum-of-product layers, support vector machines,
                 template-free symbolic performance modeling",
  ISSN =         "0278-0070",
  URL =          "http://trent.st/content/2009-TCAD-caffeine_scale.pdf",
  DOI =          "doi:10.1109/TCAD.2009.2021034",
  size =         "14 pages",
  abstract =     "This paper presents CAFFEINE, a method to
                 automatically generate compact interpretable symbolic
                 performance models of analog circuits with no prior
                 specification of an equation template. CAFFEINE uses
                 SPICE simulation data to model arbitrary nonlinear
                 circuits and circuit characteristics. CAFFEINE
                 expressions are canonical-form functions:
                 product-of-sum layers alternating with sum-of-product
                 layers, as defined by a grammar. Multiobjective genetic
                 programming trades off error with model complexity. On
                 test problems, CAFFEINE models demonstrate lower
                 prediction error than posynomials, splines, neural
                 networks, kriging, and support vector machines. This
                 paper also demonstrates techniques to scale CAFFEINE to
                 larger problems.",
  notes =        "Also known as \cite{5166638}",
}

Genetic Programming entries for Trent McConaghy Georges G E Gielen

Citations