Design of a high-gain operational amplifier and other circuits by means of genetic programming   [GP]

by

Koza, J., R., Andre, D., Bennett III, F., H. and Keane, M., A.

Literature search on Evolutionary ComputationBBase ©1999-2013, Rasmus K. Ursem
     Home · Search · Adv. search · Authors · Login · Add entries   Webmaster
Note to authors: Please submit your bibliography and contact information - online papers are more frequently cited.

Info: Evolutionary Programming VI. 6th International Conference, EP97 (Conference proceedings), 1997, p. 125-136
Keywords:genetic algorithms, genetic programming
Abstract:
This paper demonstrates that a design for a low-distortion high-gain 96 decibel (64,860-to-1) operational amplifier (including both circuit topology and component sizing) can be evolved using genetic programming. [GP]
Notes:
EP-97
URL(s):Postscript
(G)zipped postscript

Review item:

Mark as doublet (will be reviewed)

Print entry



BibTex:
@InProceedings{koza:1997:dhgopacGP,
  author =       "John R. Koza and David Andre and Forrest H {Bennett
                 III} and Martin A. Keane",
  title =        "Design of a high-gain operational amplifier and other
                 circuits by means of genetic programming",
  booktitle =    "Evolutionary Programming VI. 6th International
                 Conference, EP97",
  year =         "1997",
  editor =       "Peter J. Angeline and Robert G. Reynolds and John R.
                 McDonnell and Russ Eberhart",
  volume =       "1213",
  series =       "Lecture Notes in Computer Science",
  pages =        "125--136",
  address =      "Indianapolis, Indiana, USA",
  publisher_address = "Berlin",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/EPamp.ps",
  abstract =     "This paper demonstrates that a design for a
                 low-distortion high-gain 96 decibel (64,860-to-1)
                 operational amplifier (including both circuit topology
                 and component sizing) can be evolved using genetic
                 programming.",
  notes =        "EP-97",
}