Genetic Programming with Smooth Operators for Arithmetic Expressions: Diviplication and Subdition

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

@InProceedings{ursem:2002:gpwsofaedas,
  author =       "Rasmus K. Ursem and Thiemo Krink",
  title =        "Genetic Programming with Smooth Operators for
                 Arithmetic Expressions: Diviplication and Subdition",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and 
                 Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and 
                 Mark Shackleton",
  pages =        "1372--1377",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  month =        "12-17 " # may,
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming, arithmetic
                 expressions, search space, smooth operators, system
                 identification problem, search problems",
  URL =          "http://www.evalife.dk/publications/RKU_CEC2002_smooth_operators.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/527466.html",
  DOI =          "doi:10.1109/CEC.2002.1004443",
  abstract =     "This paper introduces the smooth operators for
                 arithmetic expressions as an approach to smoothing the
                 search space in Genetic Programming (GP). Smooth
                 operator GP interpolates between arithmetic operators
                 such as times and divide, thereby allowing a gradual
                 adaptation to the problem. The suggested approach is
                 compared to traditional GP on a system identification
                 problem",
}

Genetic Programming entries for Rasmus K Ursem Thiemo Krink

Citations