Solving the Symbolic Regression Problem with Tree-Adjunct Grammar Guided Genetic Programming: The Comparative Results

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@InProceedings{hoai:2002:stsrpwtgggptcr,
  author =       "N. X. Hoai and R. I. McKay and D. Essam and R. Chau",
  title =        "Solving the Symbolic Regression Problem with
                 Tree-Adjunct Grammar Guided Genetic Programming: The
                 Comparative Results",
  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 =        "1326--1331",
  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, TAG3P,
                 performance, structural complexity scaling, success
                 probability, symbolic regression problem, target
                 functions, tree-adjunct grammar-guided genetic
                 programming, context-free grammars, functions, problem
                 solving, programming, software performance evaluation,
                 statistical analysis, symbol manipulation, trees
                 (mathematics)",
  DOI =          "doi:10.1109/CEC.2002.1004435",
  abstract =     "In this paper, we show some experimental results of
                 tree-adjunct grammar-guided genetic programming (TAG3P)
                 on the symbolic regression problem, a benchmark problem
                 in genetic programming. We compare the results with
                 genetic programming (GP) and grammar-guided genetic
                 programming (GGGP). The results show that TAG3P
                 significantly outperforms GP and GGGP on the target
                 functions attempted in terms of the probability of
                 success. Moreover, TAG3P still performed well when the
                 structural complexity of the target function was scaled
                 up",
}

Genetic Programming entries for Nguyen Xuan Hoai R I (Bob) McKay Daryl Essam R Chau

Citations