Trading rules on stock markets using Genetic Network Programming-Sarsa Learning with plural subroutines

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Yunqing Gu and Shingo Mabu and Yang Yang and 
                 Jianhua Li and Kotaro Hirasawa",
  title =        "Trading rules on stock markets using Genetic Network
                 Programming-Sarsa Learning with plural subroutines",
  booktitle =    "Proceedings of SICE Annual Conference (SICE 2011)",
  year =         "2011",
  month =        "13-18 " # sep,
  pages =        "143--148",
  address =      "Waseda University, Tokyo, Japan",
  keywords =     "genetic algorithms, genetic programming, GNP
                 structure, automatically defined function, genetic
                 network programming-Sarsa learning, plural subroutines,
                 stock markets, subroutine node, trading rules, stock
  URL =          "",
  isbn13 =       "978-1-4577-0714-8",
  size =         "6 pages",
  abstract =     "In this paper, Genetic Network Programming-Sarsa
                 Learning (GNP-Sarsa) used for creating trading rules on
                 stock markets is enhanced by adding plural subroutines.
                 Subroutine node - a new kind of node which works like
                 ADF (Automatically Defined Function) in Genetic
                 Programming (GP) has been proved to have positive
                 effects on the stock-trading model using GNP-Sarsa. In
                 the proposed method, not only one kind of subroutine
                 but plural subroutines with different structures are
                 used to improve the performance of GNP-Sarsa with
                 subroutines. Each subroutine node could indicate its
                 own input and output node of the subroutine, which
                 could be also evolved. In the simulations, totally 16
                 brands of stock from 2001 to 2004 are used to
                 investigate the improvement of GNP-Sarsa with plural
                 subroutines. The simulation results show that the
                 proposed approach can obtain more flexible GNP
                 structure and get higher profits in stock markets.",
  notes =        "Also known as \cite{6060592}",

Genetic Programming entries for Yunqing Gu Shingo Mabu Yang Yang Jianhua Li Kotaro Hirasawa