Routine automated synthesis of five patented analog circuits using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "J. R. Koza and M. A. Keane and M. J. Streeter",
  title =        "Routine automated synthesis of five patented analog
                 circuits using genetic programming",
  journal =      "Soft Computing - A Fusion of Foundations,
                 Methodologies and Applications",
  year =         "2004",
  volume =       "8",
  number =       "5",
  pages =        "318--324",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, Automatic
                 circuit synthesis, Evolvable hardware, Automated
                 design, Artificial intelligence",
  ISSN =         "1432-7643",
  DOI =          "doi:10.1007/s00500-003-0288-9",
  abstract =     "This article reports on a project in which we browsed
                 patents issued after January 1, 2000 to commercial
                 enterprises or university research institutions for
                 analog electrical circuits. We then employed genetic
                 programming to automatically design (synthesise)
                 entities that duplicated the functionality of five
                 post-2000 issued patents. The automated method works
                 from a high-level statement of the circuit's intended
                 function. The article addresses the question of what is
                 actually delivered by the operation of the artificial
                 problem-solving method in relation to the amount of
                 intelligence that is supplied by the humans employing
                 the method (something we refer to as the yield of an
                 automated method). The article also addresses the
                 question of the routineness of the artificial
                 problem-solving method ? that is, the amount of effort
                 required to make the transition from problem to problem
                 within a particular domain. The conclusion is that the
                 artificial method routinely delivers high-yield,
                 human-competitive (i.e., previously patented)

Genetic Programming entries for John Koza Martin A Keane Matthew J Streeter