Double-Strength CAFFEINE: Fast Template-Free Symbolic Modeling of Analog Circuits via Implicit Canonical Form Functions and Explicit Introns

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{McConaghy:2006:DATE,
  author =       "Trent McConaghy and Georges Gielen",
  title =        "Double-Strength CAFFEINE: Fast Template-Free Symbolic
                 Modeling of Analog Circuits via Implicit Canonical Form
                 Functions and Explicit Introns",
  booktitle =    "Proceedings of Design, Automation and Test in Europe,
                 DATE '06",
  year =         "2006",
  volume =       "1",
  address =      "Munich",
  month =        "6-10 " # mar,
  keywords =     "genetic algorithms, genetic programming, EHW, SPICE,
                 analogue circuits, circuit simulation, evolutionary
                 computation, optimisation, SPICE, analog circuit
                 modelling, canonical functional form expressions in
                 evolution, double-strength CAFFEINE method, explicit
                 introns, Analog circuits, Circuit optimisation, Circuit
                 simulation, Circuit testing, Circuit topology, Context
                 modelling, Mathematical model, Nonlinear circuits,
                 Predictive models",
  ISBN =         "3-9810801-1-4",
  URL =          "http://trent.st/content/2006-DATE-caffeine_double.pdf",
  DOI =          "doi:10.1109/DATE.2006.244136",
  size =         "6 pages",
  abstract =     "CAFFEINE, introduced previously, automatically
                 generates nonlinear, template-free symbolic performance
                 models of analog circuits from SPICE data. Its key was
                 a directly-interpretable functional form, found via
                 evolutionary search. In application to automated sizing
                 of analog circuits, CAFFEINE was shown to have the best
                 predictive ability from among 10 regression techniques,
                 but was too slow to be used practically in the
                 optimisation loop. In this paper, we describe
                 double-strength CAFFEINE, which is designed to be fast
                 enough for automated sizing, yet retain good predictive
                 abilities. We design smooth, uniform search operators
                 which have been shown to greatly improve efficiency in
                 other domains. Such operators are not straightforward
                 to design; we achieve them in functions by
                 simultaneously making the grammar-constrained
                 functional form implicit, and embedding explicit
                 'introns' (subfunctions appearing in the candidate that
                 are not expressed). Experimental results on six test
                 problems show that double-strength CAFFEINE achieves an
                 average speedup of 5times on the most challenging
                 problems and 3times overall; thus making the technique
                 fast enough for automated sizing",
  notes =        "Also known as \cite{1656888}",
}

Genetic Programming entries for Trent McConaghy Georges G E Gielen

Citations