Synthesis of neural tree models by improved breeder genetic programming

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  title =        "Synthesis of neural tree models by improved breeder
                 genetic programming",
  author =       "Feng Qi and Xiyu Liu and Yinghong Ma",
  journal =      "Neural Computing and Applications",
  year =         "2012",
  number =       "3",
  volume =       "21",
  pages =        "515--521",
  keywords =     "genetic algorithms, genetic programming, neural tree
                 model, noisy fitness evaluation, improved breeder
                 genetic programming, Gaussian mutation, time series",
  DOI =          "doi:10.1007/s00521-010-0451-z",
  size =         "7 pages",
  abstract =     "Neural tree model has been successfully applied to
                 solving a variety of interesting problems. In most
                 previous studies, optimisation of the neural tree model
                 was divided into two steps: first structure
                 optimisation, then parameter optimisation. One major
                 problem in the evolution of structure without parameter
                 information was noisy fitness evaluation. In this
                 paper, an improved breeder genetic programming
                 algorithm is proposed to the synthesis of neural tree
                 model. The effectiveness and performance of the method
                 are evaluated on time series prediction problems and
                 compared with those of related methods. Simulation
                 results show that the proposed algorithm is a potential
                 method with better performance and effectiveness.",
  notes =        "Mackey-Glass",
  affiliation =  "School of Management and Economics, Shandong Normal
                 University, Jinan, China",
  bibdate =      "2012-03-20",
  bibsource =    "DBLP,

Genetic Programming entries for Feng Qi Xiyu Liu Yinghong Ma