Automatic program generation with genetic network programming using subroutines

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

  author =       "Bing Li and Shingo Mabu and Kotaro Hirasawa",
  title =        "Automatic program generation with genetic network
                 programming using subroutines",
  booktitle =    "Proceedings of SICE Annual Conference 2010",
  year =         "2010",
  address =      "Taipei, Taiwan",
  month =        "18-21 " # aug,
  pages =        "3089--3094",
  organisation = "SICE",
  keywords =     "genetic algorithms, genetic programming, automatic
                 program generation, evolutionary algorithm, genetic
                 network programming, genotype phenotype mapping
                 technology, graph based structure, subroutine program,
                 automatic programming, performance evaluation,
  isbn13 =       "978-1-4244-7642-8",
  URL =          "",
  abstract =     "Genetic Network Programming with Automatic Program
                 Generation (GNP-APG) is an evolutionary algorithm to
                 generate programs. Genotype-phenotype mapping
                 technology is introduced in this algorithm to create
                 legal programs. With the help of graph-based structures
                 of Genetic Network Programming (GNP), GNP-APG can
                 efficiently generate robust programs to cope with
                 problems. In this paper, the extended algorithm of
                 GNP-APG is proposed which can create a hierarchy
                 program, in other words, a program which contains a
                 main function and subroutines. The proposed method
                 works like Automatic Defined Functions (ADFs) in
                 Genetic Programming (GP). By using subroutines, a
                 complex program can be decomposed to several simple
                 programs which are obtained more easily. Moreover,
                 these subroutines might be called many times, which
                 results in reducing the size of the program
                 significantly. In simulations, different tile-worlds
                 between the training phase and testing phase are used
                 for performance evaluations and the results shows that
                 GNP-APG with subroutines (GNP-APGsr) could have better
                 performances than GNP-APG.",
  notes =        "tile world. Grad. Sch. of Inf., Production & Syst.,
                 Waseda Univ., Fukuoka, Japan. Also known as

Genetic Programming entries for Bing Li Shingo Mabu Kotaro Hirasawa