Automatic creation of both the topology and parameters for a robust controller by means of genetic programming

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

  author =       "John R. Koza and Martin A. Keane and 
                 Forrest H {Bennett III} and Jessen Yu and William Mydlowec and 
                 Oscar Stiffelman",
  title =        "Automatic creation of both the topology and parameters
                 for a robust controller by means of genetic
  booktitle =    "Proceedings of the 1999 IEEE International Symposium
                 on Intelligent Control, Intelligent Systems, and
  year =         "1999",
  pages =        "344--352",
  publisher_address = "Piscataway, NJ, USA",
  organisation = "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  abstract =     "The paper describes a general automated method for
                 synthesizing the design of both the topology and
                 parameter values for controllers. The automated method
                 automatically makes decisions concerning the total
                 number of processing blocks to be employed in the
                 controller, the type of each block, the topological
                 interconnections between the blocks, the values of all
                 parameters for the blocks, and the existence, if any,
                 of internal feedback between the blocks of the overall
                 controller. Incorporation of time-domain,
                 frequency-domain, and other constraints on the control
                 or state variables (often analytically intractable
                 using conventional methods) can be readily
                 accommodated. The automatic method described in the
                 paper (genetic programming) is applied to a problem of
                 synthesizing the design of a robust controller for a
                 plant with a second-order lag. A textbook PID
                 compensator preceded by a lowpass pre-filter delivers
                 credible performance on this problem. However, the
                 automatically created controller employs a second
                 derivative processing block (in addition to
                 proportional, integrative, and derivative blocks and a
                 pre-filter). It is better than twice as effective as
                 the textbook controller as measured by the integral of
                 the time-weighted absolute error, has only two-thirds
                 of the rise time in response to the reference (command)
                 input, and is 10 times better in terms of suppressing
                 the effects of disturbance at the plant input.",
  notes =        "IEEE ISIC-99",

Genetic Programming entries for John Koza Martin A Keane Forrest Bennett Jessen Yu William J Mydlowec Oscar Stiffelman