Symbolic regression methods for control system synthesis

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

  author =       "Askat Diveev and David Kazaryan and Elena Sofronova",
  booktitle =    "22nd Mediterranean Conference of Control and
                 Automation (MED 2014)",
  title =        "Symbolic regression methods for control system
  year =         "2014",
  month =        "16-19 " # jun,
  pages =        "587--592",
  abstract =     "In this paper we use symbolic regression methods for
                 control system synthesis. We compare three methods:
                 network operator method, genetic programming and
                 analytical programming. We developed variational
                 versions of genetic programming and analytical
                 programming to improve the search process efficiency.
                 All the methods perform search over the set of the
                 small variations of the given basic solution. Search
                 efficiency depends on the basic solution. We give an
                 example of control system synthesis for the unmanned
                 vehicle with the state constraints over the set of the
                 initial states using these methods.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/MED.2014.6961436",
  notes =        "Also known as \cite{6961436}",

Genetic Programming entries for Askat Diveev David Kazaryan Elena A Sofronova