A Variable Size Mechanism of Distributed Graph Programs for Creating Agent Behaviors

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

  article_id =   "1137",
  author =       "Shingo Mabu and Kotaro Hirasawa and 
                 Masanao Obayashi and Takashi Kuremoto",
  title =        "A Variable Size Mechanism of Distributed Graph
                 Programs for Creating Agent Behaviors",
  booktitle =    "2013 IEEE Conference on Evolutionary Computation",
  volume =       "1",
  year =         "2013",
  month =        jun # " 20-23",
  editor =       "Luis Gerardo {de la Fraga}",
  pages =        "1756--1762",
  address =      "Cancun, Mexico",
  keywords =     "genetic algorithms, genetic programming, GNP",
  isbn13 =       "978-1-4799-0453-2",
  DOI =          "doi:10.1109/CEC.2013.6557773",
  size =         "7 pages",
  abstract =     "Genetic Algorithm (GA) and Genetic Programming (GP)
                 are typical evolutionary algorithms using string and
                 tree structures, respectively, and there have been many
                 studies on the extension of GA and GP. How to represent
                 solutions, e.g., strings, trees, graphs, etc., is one
                 of the important research topics and Genetic Network
                 Programming (GNP) has been proposed as one of the
                 graph-based evolutionary algorithms. GNP represents its
                 solutions using directed graph structures and has been
                 applied to many applications. However, when GNP is
                 applied to complex real world systems, large size of
                 the programs is needed to represent various kinds of
                 control rules. In this case, the efficiency of
                 evolution and the performance of the systems may
                 decrease due to its huge structures. Therefore,
                 distributed GNP has been studied based on the idea of
                 divide and conquer, where the programs are divided into
                 several subprograms and they cooperatively control
                 whole tasks. However, because the previous work divided
                 a program into some subprograms with the same size, it
                 cannot adjust the sizes of the subprograms depending on
                 the problems. Therefore, in this paper, an efficient
                 evolutionary algorithm of variable size distributed GNP
                 is proposed and its performance is evaluated by the
                 tileworld problem that is one of the benchmark problems
                 of multiagent systems in dynamic environments.",
  notes =        "CEC 2013 - A joint meeting of the IEEE, the EPS and
                 the IET.",

Genetic Programming entries for Shingo Mabu Kotaro Hirasawa Masanao Obayashi Takashi Kuremoto