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

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@Article{Nguyen:2001:AJIIPS,
  author =       "X. H. Nguyen and R. I. (Bob) McKay and D. L. Essam",
  journal =      "The Australian Journal of Intelligent Information
                 Processing Systems",
  number =       "3/4",
  pages =        "114--121",
  title =        "Solving the Symbolic Regression Problem with
                 Tree-Adjunct Grammar Guided Genetic Programming: The
                 Comparative Results",
  URL =          "http://sc.snu.ac.kr/PAPERS/xuanetal.pdf",
  volume =       "7",
  year =         "2001",
  keywords =     "genetic algorithms, genetic programming",
  size =         "6 pages",
  abstract =     "In this paper, we show some experimental results of
                 tree-adjunct grammar guided genetic programming [6]
                 (TAG3P) on the symbolic regression problem, a benchmark
                 problem in genetic programming. We compare the results
                 with genetic programming [9] (GP) and grammar guided
                 genetic programming [14] (GGGP). The results show that
                 TAG3P significantly outperforms GP and GGGP on the
                 target functions attempted in terms of 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

Citations