Function Sequence Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  title =        "Function Sequence Genetic Programming",
  author =       "Shixian Wang and Yuehui Chen and Peng Wu",
  booktitle =    "5th International Conference on Intelligent Computing,
                 ICIC 2009",
  year =         "2009",
  volume =       "5755",
  editor =       "De-Shuang Huang and Kang-Hyun Jo and Hong-Hee Lee and 
                 Hee-Jun Kang and Vitoantonio Bevilacqua",
  series =       "Lecture Notes in Computer Science",
  pages =        "984--992",
  address =      "Ulsan, South Korea",
  month =        sep # " 16-19",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Function
                 Sequence Genetic Programming, factorial problem, stock
                 index prediction",
  isbn13 =       "978-3-642-04019-1",
  DOI =          "doi:10.1007/978-3-642-04020-7_106",
  bibdate =      "2009-09-25",
  bibsource =    "DBLP,
  abstract =     "Genetic Programming(GP) can obtain a program structure
                 to solve complex problem. This paper presents a new
                 form of Genetic Programming, Function Sequence Genetic
                 Programming (FSGP). We adopt function set like Genetic
                 Programming, and define data set corresponding to its
                 terminal set. Besides of input data and constants, data
                 set include medium variables which are used not only as
                 arguments of functions, but also as temporary variables
                 to store function return value. The program individual
                 is given as a function sequence instead of tree and
                 graph. All functions run orderly. The result of
                 executed program is the return value of the last
                 function in the function sequences. This presentation
                 is closer to real handwriting program. Moreover it has
                 an advantage that the genetic operations are easy
                 implemented since the function sequence is linear. We
                 apply FSGP to factorial problem and stock index
                 prediction. The initial simulation results indicate
                 that the FSGP is more powerful than the conventional
                 genetic programming both in implementation time and
                 solution accuracy.",

Genetic Programming entries for Shixian Wang Yuehui Chen Peng Wu