Genetic Programming in Industrial Analog CAD: Applications and Challenges

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Trent McConaghy and Georges Gielen",
  title =        "Genetic Programming in Industrial Analog {CAD}:
                 {Applications} and Challenges",
  booktitle =    "Genetic Programming Theory and Practice {III}",
  year =         "2005",
  editor =       "Tina Yu and Rick L. Riolo and Bill Worzel",
  volume =       "9",
  series =       "Genetic Programming",
  chapter =      "19",
  pages =        "291--306",
  address =      "Ann Arbor",
  month =        "12-14 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Analogue,
                 CAD, Synthesis, Industrial, Robust, Yield",
  ISBN =         "0-387-28110-X",
  oai =          "oai:CiteSeerX.psu:",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1007/0-387-28111-8_19",
  size =         "16 pages",
  abstract =     "This paper investigates the application of genetic
                 programming to problems in industrial analog
                 computer-aided design (CAD). One CAD subdomain,
                 analogue structural synthesis, is an often-cited
                 success within the genetic programming (GP) literature,
                 yet industrial use remains elusive. We examine why this
                 is, by drawing upon our own experiences in bringing
                 analogue CAD tools into industrial use. In sum,
                 GP-synthesised designs need to be more robust in very
                 specific ways. When robustness is considered, a GP
                 methodology of today on a reasonable circuit problem
                 would take 150 years on a 1,000-node 1-GHz cluster.
                 Moore's Law cannot help either, because the problem
                 itself is 'Anti-Mooreware' -- it becomes more difficult
                 as Moore's Law progresses. However, we believe the
                 problem is still approachable with GP; it will just
                 take a significant amount of 'algorithm

                 We go on to describe the recent application of GP to
                 two other analogue CAD subdomains: symbolic modelling
                 and behavioural modeling. In contrast to structural
                 synthesis, they are easier from a GP perspective, but
                 are already at a level such that they can be exploited
                 in industry. Not only is GP the only approach that
                 gives interpretable SPICE-accurate nonlinear models, it
                 turns out to outperform nine other popular blackbox
                 approaches in a set of six circuit modeling problems.",
  notes =        "part of \cite{yu:2005:GPTP} Published Jan 2006 after
                 the workshop",

Genetic Programming entries for Trent McConaghy Georges G E Gielen