Grammar Guided Genetic Programming for Flexible Neural Trees Optimization

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  author =       "Peng Wu and Yuehui Chen",
  title =        "Grammar Guided Genetic Programming for Flexible Neural
                 Trees Optimization",
  booktitle =    "Proceedings of the 11th Pacific-Asia Conference on
                 Knowledge Discovery and Data Mining, PAKDD 2007",
  year =         "2007",
  editor =       "Zhi-Hua Zhou and Hang Li and Qiang Yang",
  volume =       "4426",
  series =       "Lecture Notes in Computer Science",
  pages =        "964--971",
  address =      "Nanjing, China",
  month =        may # " 22-25",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-71700-3",
  DOI =          "doi:10.1007/978-3-540-71701-0_108",
  size =         "8 pages",
  abstract =     "In our previous studies, Genetic Programming (GP),
                 Probabilistic Incremental Program Evolution (PIPE) and
                 Ant Programming (AP) have been used to optimal design
                 of Flexible Neural Tree (FNT). In this paper Grammar
                 Guided Genetic Programming (GGGP) was employed to
                 optimize the architecture of FNT model. Based on the
                 pre-defined instruction sets, a flexible neural tree
                 model can be created and evolved. This framework allows
                 input variables selection, over-layer connections and
                 different activation functions for the various nodes
                 involved. The free parameters embedded in the neural
                 tree are optimized by particle swarm optimization
                 algorithm. Empirical results on stock index prediction
                 problems indicate that the proposed method is better
                 than the neural network and genetic programming
                 forecasting models.",
  bibdate =      "2007-06-24",
  bibsource =    "DBLP,

Genetic Programming entries for Peng Wu Yuehui Chen