A New Circuit Representation Method for Analog Circuit Design Automation

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Feng Wang and Yuanxiang Li and Kangshun Li and 
                 Zhiyi Lin",
  title =        "A New Circuit Representation Method for Analog Circuit
                 Design Automation",
  booktitle =    "2008 IEEE World Congress on Computational
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "1976--1980",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0475.pdf",
  DOI =          "doi:10.1109/CEC.2008.4631059",
  abstract =     "The Analog circuits are very important in many
                 high-speed applications such as communications. Since
                 the size of analog circuit is becoming larger and more
                 complex, the design is becoming more and more
                 difficult. This paper proposes a new circuit
                 representation method based on a two layer evolutionary
                 scheme with Genetic Programming (TLGP), which uses a
                 divide-and-conquer approach to evolve the analog
                 circuits. This representation has the desirable
                 property which is more helpful to generate expectant
                 circuit graphs. And it is capable of generating various
                 kinds of circuits by evolving the circuits with
                 dynamical size, circuit topology, and component values.
                 The experimental results on the designs of the voltage
                 amplifier and the low-pass filter show that this method
                 is efficient.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",

Genetic Programming entries for Feng Wang Yuanxiang Li Kangshun Li Zhiyi Lin