A new hybrid structure genetic programming in symbolic regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Xiong Shengwu and Wang Weiwu",
  title =        "A new hybrid structure genetic programming in symbolic
  booktitle =    "Proceedings of the 2003 Congress on Evolutionary
                 Computation CEC2003",
  editor =       "Ruhul Sarker and Robert Reynolds and 
                 Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and 
                 Tom Gedeon",
  pages =        "1500--1506",
  year =         "2003",
  publisher =    "IEEE Press",
  address =      "Canberra",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "8-12 " # dec,
  organisation = "IEEE Neural Network Council (NNC), Engineers Australia
                 (IEAust), Evolutionary Programming Society (EPS),
                 Institution of Electrical Engineers (IEE)",
  keywords =     "genetic algorithms, genetic programming, Arithmetic,
                 Computer science, Convergence of numerical methods,
                 Evolutionary computation, Fractals, Modelling,
                 Regression analysis, Shape, Time varying systems,
                 regression analysis, GP representation, complex system
                 modelling, continuous function, discontinuity points,
                 discontinuous function, function regression, function
                 structure, hybrid structure genetic programming,
                 nonsmooth function, smooth function, symbolic
  DOI =          "doi:10.1109/CEC.2003.1299850",
  ISBN =         "0-7803-7804-0",
  abstract =     "Genetic Programming (GP) has been applied to symbolic
                 regression problem for a long time. The symbolic
                 regression is to discover a function that can fit a
                 finite set of sample data. These sample data can be
                 guided by a simple function, which is continuous and
                 smooth. But in a complex system, they can be produced
                 by a discontinuous or non-smooth function. When
                 conventional GP is applied to this complex system's
                 modeling, it gets poor performance. This paper proposes
                 a new GP representation and algorithm that can be
                 applied to both continuous function's and discontinuous
                 function's regression. Our approach is able to identify
                 both simultaneously the function's structure and the
                 discontinuity points. The numerical experimental
                 results will show that the new GP is able to gain
                 higher success rate, higher convergence rate and better
                 solutions than conventional GP.",
  notes =        "CEC 2003 - A joint meeting of the IEEE, the IEAust,
                 the EPS, and the IEE.",

Genetic Programming entries for Shengwu Xiong Weiwu Wang