A MEP and IP Based Flexible Neural Tree Model for Exchange Rate Forecasting

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  author =       "Guangfeng Jia and Yuehui Chen and Qiang Wu",
  title =        "A MEP and IP Based Flexible Neural Tree Model for
                 Exchange Rate Forecasting",
  booktitle =    "Fourth International Conference on Natural
                 Computation, ICNC '08",
  year =         "2008",
  month =        oct,
  volume =       "5",
  pages =        "299--303",
  keywords =     "genetic algorithms, genetic programming, MEP,
                 financial problem, flexible neural tree model, foreign
                 exchange rate forecasting, immune programming, multi
                 expression programming, exchange rates, financial
                 management, neural nets, trees (mathematics)",
  DOI =          "doi:10.1109/ICNC.2008.669",
  abstract =     "Forecasting exchange rate is an important financial
                 problem that is received much more attentions because
                 of its difficulty and practical applications. The
                 problem of prediction of foreign exchange rates by
                 using multi expression programming and immune
                 programming based flexible neural tree (MEPIP-FNT) is
                 presented in this paper. This work is an extension of
                 our previously traditional FNT model which can optimize
                 the architectures and the weights of flexible neuron
                 model respectively. The novel MEPIPFNT model with the
                 underlying immune theories is capable of evolving the
                 architectures and the weights simultaneously. To
                 demonstrate the efficiency of the model, we conduct
                 three different datasets in our forecasting performance
  notes =        "Also known as \cite{4667445}",

Genetic Programming entries for Guangfeng Jia Yuehui Chen Qiang Wu