Analog Circuit Design Automation Using Neural Network-Based Two-Level Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Wang:2006:MLC,
  author =       "Feng Wang and Yuan-Xiang Li",
  title =        "Analog Circuit Design Automation Using Neural
                 Network-Based Two-Level Genetic Programming",
  booktitle =    "2006 International Conference on Machine Learning and
                 Cybernetics",
  year =         "2006",
  pages =        "2087--2092",
  address =      "Dalian",
  month =        aug,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-4244-0061-9",
  DOI =          "doi:10.1109/ICMLC.2006.258348",
  abstract =     "The design of analog circuits starts with a high-level
                 statement of the circuit's desired behaviour and
                 requires creating a circuit that satisfies the
                 specified design goals. The difficulty of the problem
                 of analog circuit design is well known, and there is no
                 previously known general automated technique to design
                 an analog circuit from a high-level statement of the
                 circuit's desired behaviour. This paper proposes a
                 two-layer evolutionary scheme based on Genetic
                 Programming (GP) and Neural Network (NN), which uses a
                 divide-and-conquer approach to design the analog
                 circuits. Corresponding to the NN-TLGP, a new
                 representation of circuit has been proposed here and it
                 is more helpful to generate expectant circuit graphs.
                 This algorithm can perform the circuits with dynamical
                 size, circuit topology, and component values. The
                 experimental results on the two design work show that
                 this algorithm is efficient.",
  notes =        "Department of Computer Science, Wuhan University,
                 Wuhan, 430072, China; State Key Lab of Software
                 Engineering, Wuhan University, Wuhan, 430072, China.",
}

Genetic Programming entries for Feng Wang Yuanxiang Li

Citations