Dynamic Ant Programming for Automatic Construction of Programs

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

  author =       "Shinichi Shirakawa and Shintaro Ogino and 
                 Tomoharu Nagao",
  title =        "Dynamic Ant Programming for Automatic Construction of
  journal =      "IEEJ Transactions on Electrical and Electronic
                 Engineering (TEEE)",
  year =         "2008",
  volume =       "3",
  number =       "5",
  pages =        "540--548",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, automatic
                 programming, ant colony optimization, swarm
  URL =          "http://www3.interscience.wiley.com/search/allsearch?mode=viewselected&product=journal&ID=121387914&view_selected.x=42&view_selected.y=7&view_selected=view_selected",
  DOI =          "doi:10.1002/tee.20311",
  size =         "9 pages",
  abstract =     "A new method for automatic programming is proposed in
                 this paper. Automatic programming is the method of
                 generating computer programs automatically. Genetic
                 programming (GP) is a typical example of automatic
                 programming. GP evolves computer programs with tree
                 structure based on genetic algorithm (GA). The new
                 method is named dynamic ant programming (DAP). DAP is
                 based on ant colony optimization (ACO) and uses
                 dynamically changing pheromone table. The nodes
                 (terminal and nonterminal) are selected using the value
                 of pheromone table. The higher the rate of pheromone,
                 the higher is the probability that it can be chosen.
                 Although the search space (i.e., the pheromone table of
                 DAP) is dynamically changing, the ants find good
                 solution using portions of solutions, which are of
                 pheromone value. We describe the method of construction
                 of tree structure using ACO, as well as pheromone
                 update and deletion and insertion of nodes in detail.
                 DAP is applied to the symbolic regression problem that
                 is widely used as a test problem for GP system. We
                 compare the performance of DAP to GP and show the
                 effectiveness of DAP. In order to investigate the
                 influence of several parameters, we compare
                 experimental results obtained using different

Genetic Programming entries for Shinichi Shirakawa Shintaro Ogino Tomoharu Nagao