ISCLEs: Importance Sampled Circuit Learning Ensembles for Trustworthy Analog Circuit Topology Synthesis

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@InProceedings{Gao:2008:ICES,
  author =       "Peng Gao and Trent McConaghy and Georges Gielen",
  title =        "{ISCLEs:} Importance Sampled Circuit Learning
                 Ensembles for Trustworthy Analog Circuit Topology
                 Synthesis",
  booktitle =    "Proceedings of the 8th International Conference on
                 Evolvable Systems, ICES 2008",
  year =         "2008",
  editor =       "Gregory S. Hornby and Lukas Sekanina and 
                 Pauline C. Haddow",
  volume =       "5216",
  series =       "Lecture Notes in Computer Science",
  pages =        "11--21",
  address =      "Prague, Czech Republic",
  month =        sep # " 21-24",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, EHW",
  isbn13 =       "978-3-540-85856-0",
  URL =          "http://trent.st/content/2008-ICES-iscles.pdf",
  DOI =          "doi:10.1007/978-3-540-85857-7_2",
  size =         "11 pages",
  abstract =     "Importance Sampled Circuit Learning Ensembles (ISCLEs)
                 is a novel analog circuit topology synthesis method
                 that returns designer-trustworthy circuits yet can
                 apply to a broad range of circuit design problems
                 including novel functionality. ISCLEs uses the machine
                 learning technique of boosting, which does importance
                 sampling of weak learners to create an overall circuit
                 ensemble. In ISCLEs, the weak learners are circuit
                 topologies with near-minimal transistor sizes. In each
                 boosting round, first a new weak learner topology and
                 sizings are found via genetic programming-based MOJITO
                 multi-topology optimisation, then it is combined with
                 previous learners into an ensemble, and finally the
                 weak-learning target is updated. Results are shown for
                 the trustworthy synthesis of a sinusoidal function
                 generator, and a 3-bit A/D converter.",
  notes =        "Evolvable Systems: From Biology to Hardware",
}

Genetic Programming entries for Peng Gao Trent McConaghy Georges G E Gielen

Citations