System identification approach to genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Iba:1994:siGP,
  author =       "Hitoshi Iba and Taisuke Sato and Hugo {de Garis}",
  title =        "System identification approach to genetic
                 programming",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  pages =        "401--406",
  volume =       "1",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Boolean
                 concept formation, STROGANOFF, adaptive program,
                 adaptive search, local parameter tuning mechanism,
                 minimum description length-based selection criterion,
                 multiple node types, multiple regression analysis,
                 nonlinear function fitting, nonnumerical reasoning,
                 numerical problems, statistical search, structured
                 representation, symbolic reasoning, symbolic regression
                 problems, system identification, tree pruning, tree
                 structures, Boolean functions, identification, search
                 problems, statistical analysis, symbol manipulation,
                 trees (mathematics), tuning",
  size =         "6 pages",
  DOI =          "doi:10.1109/ICEC.1994.349917",
  abstract =     "Introduces a new approach to genetic programming (GP),
                 based on a system identification technique, which
                 integrates a GP-based adaptive search of tree
                 structures and a local parameter tuning mechanism
                 employing a statistical search. In Proc. 5th Int. Joint
                 Conf. on Genetic Algorithms (1993), we introduced our
                 adaptive program called STROGANOFF (STructured
                 Representation On Genetic Algorithms for NOnlinear
                 Function Fitting), which integrated a multiple
                 regression analysis method and a GA-based search
                 strategy. The effectiveness of STROGANOFF was
                 demonstrated by solving several system identification
                 (numerical) problems. This paper extends STROGANOFF to
                 symbolic (non-numerical) reasoning, by introducing
                 multiple types of nodes, using a modified minimum
                 description length (MDL) based selection criterion, and
                 a pruning of the resultant trees. The effectiveness of
                 this system-identification approach to GP is
                 demonstrated by successful application to Boolean
                 concept formation and to symbolic regression problems",
}

Genetic Programming entries for Hitoshi Iba Taisuke Sato Hugo de Garis

Citations