Cartesian Ant Programming with adaptive node replacements

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

  author =       "Akira Hara and Jun-ichi Kushida and Keita Fukuhara and 
                 Tetsuyuki Takahama",
  booktitle =    "7th IEEE International Workshop on Computational
                 Intelligence and Applications (IWCIA 2014)",
  title =        "Cartesian Ant Programming with adaptive node
  year =         "2014",
  month =        nov,
  pages =        "119--124",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, ACO, swarm intelligence",
  DOI =          "doi:10.1109/IWCIA.2014.6988089",
  ISSN =         "1883-3977",
  size =         "6 pages",
  abstract =     "Ant Colony Optimisation (ACO) is a swarm-based search
                 method. Multiple ant agents search various solutions
                 and their searches focus on around good solutions by
                 positive feedback mechanism based on pheromone
                 communication. ACO is effective for combinatorial
                 optimisation problems. The attempt of applying ACO to
                 automatic programming has been studied in recent years.
                 As one of the attempts, we have previously proposed
                 Cartesian Ant Programming (CAP) as an ant-based
                 automatic programming method. Cartesian Genetic
                 Programming (CGP) is well-known as an evolutionary
                 optimisation method for graph-structural programs. CAP
                 combines graph representations in CGP with pheromone
                 communication in ACO. The connections of program
                 primitives, terminal and functional symbols, can be
                 optimised by ants. CAP showed better performance than
                 CGP. However, quantities of respective symbols are
                 limited due to the fixed assignments of functional
                 symbols to nodes. Therefore, if the number of given
                 nodes is not enough for representing program, the
                 search performance becomes poor. In this paper, to
                 solve the problem, we propose CAP with adaptive node
                 replacements. This method finds unnecessary nodes which
                 are not used for representing programs. Then, new
                 functional symbols, which seems to be useful for
                 constructing good programs, are assigned to the nodes.
                 By this method, given nodes can be used efficiently. In
                 order to examine the effectiveness of our method, we
                 apply it to a symbolic regression problem. CAP with
                 adaptive node replacements showed better results than
                 conventional methods, CGP and CAP.",
  notes =        "Also known as \cite{6988089}",

Genetic Programming entries for Akira Hara Jun-ichi Kushida Keita Fukuhara Tetsuyuki Takahama