A new genetic programming approach in symbolic regression

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

  author =       "Shengwu Xiong and Weiwu Wang and Feng Li",
  title =        "A new genetic programming approach in symbolic
  booktitle =    "Proceedings 15th IEEE International Conference on
                 Tools with Artificial Intelligence",
  year =         "2003",
  pages =        "161--165",
  month =        "3-5 " # nov,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1082-3409",
  URL =          "http://ieeexplore.ieee.org/iel5/8840/27974/01250185.pdf?tp=&arnumber=1250185&isnumber=27974",
  DOI =          "doi:10.1109/TAI.2003.1250185",
  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, the sample data can be
                 produced by a discontinuous or non-smooth function.
                 When conventional GP is applied to such complex
                 system's regression, it gets poor performance. This
                 paper proposed a new GP representation and algorithm
                 that can be applied to both continuous function's
                 regression and discontinuous function's regression. The
                 proposed approach is able to identify both the
                 sub-functions and the discontinuity points
                 simultaneously. The numerical experimental results show
                 that the new GP is able to obtain higher success rate,
                 higher convergence rate and better solutions than
                 conventional GP in such complex system's regression.",
  notes =        "Sch. of Comput. Sci. & Technol., Wuhan Univ. of
                 Technol., China",

Genetic Programming entries for Shengwu Xiong Weiwu Wang Feng Li