Design of Posynomial Models for Mosfets: Symbolic Regression Using Genetic Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Varun Aggarwal and Una-May O'Reilly",
  title =        "Design of Posynomial Models for Mosfets: Symbolic
                 Regression Using Genetic Algorithms",
  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 =        "219--236",
  address =      "Ann Arbor",
  month =        "11-13 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, circuit
                 sizing, symbolic regression, posynomial models,
                 geometric programming",
  ISBN =         "0-387-33375-4",
  URL =          "",
  DOI =          "doi:10.1007/978-0-387-49650-4_14",
  size =         "19 pages",
  abstract =     "Starting from a broad description of analogue circuit
                 design in terms of topology design and sizing, we
                 discuss the difficulties of sizing and describe
                 approaches that are manual or automatic. These
                 approaches make use of blackbox optimisation techniques
                 such as evolutionary algorithms or convex optimization
                 techniques such as geometric programming. Geometric
                 programming requires posynomial expressions for a
                 circuit's performance measurements. We show how a
                 genetic algorithm can be exploited to evolve a
                 polynomial expression (i.e. model) of transistor (i.e.
                 mosfet) behaviour more accurately than statistical
                 techniques in the literature.",
  notes =        "part of \cite{Riolo:2006:GPTP} Published Jan 2007
                 after the workshop",

Genetic Programming entries for Varun Aggarwal Una-May O'Reilly