Efficiency improvement of imitation operator in multi-agent control model based on Cartesian Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Hara:2016:IWCIA,
  author =       "Akira Hara and Hiroki Konishi and Jun-ichi Kushida and 
                 Tetsuyuki Takahama",
  title =        "Efficiency improvement of imitation operator in
                 multi-agent control model based on Cartesian Genetic
                 Programming",
  booktitle =    "2016 IEEE 9th International Workshop on Computational
                 Intelligence and Applications",
  year =         "2016",
  editor =       "Shimpei Matsumoto and Tomoko Tateyam",
  pages =        "69--74",
  address =      "Hiroshima",
  month =        "5 " # nov,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 genetic programming",
  DOI =          "doi:10.1109/IWCIA.2016.7805751",
  abstract =     "In this paper, we focus on evolutionary optimization
                 of multi-agent behaviour. In our previous work, we have
                 proposed a multi-agent control model based on Cartesian
                 Genetic Programming (CGP). In CGP, each individual is
                 represented by a graph-structural program. The CGP has
                 a characteristics that each individual has multiple
                 output nodes. Therefore, by assigning the outputs to
                 respective agents, we can control multiple agents by an
                 individual. The method enables multiple agents to not
                 only take different actions according to their own
                 roles but also share sub-programs if the same behaviour
                 is needed for solving problems. In addition, a new
                 genetic operator for multi-agent control, imitation
                 operator, has been proposed to facilitate the grouping
                 of agents. An agent selects another agent at random for
                 imitating the behavior. However, if the number of
                 agents increases, the appropriate agent cannot always
                 be selected for imitation. Therefore, in this paper, we
                 propose a modified imitation operator for selecting
                 useful agent. We applied our method to a food foraging
                 problem. The experimental results showed that the
                 performance of our method is superior to those of the
                 conventional models.",
  notes =        "http://www.smc-hiroshima.info.hiroshima-cu.ac.jp/events/iwcia/2016/contr_program.html

                 Graduate School of Information Sciences, Hiroshima City
                 University, 3-4-1, Ozuka-higashi, Asaminami-ku,
                 Hiroshima, Japan 731-3194

                 Also known as \cite{7805751}",
}

Genetic Programming entries for Akira Hara Hiroki Konishi Jun-ichi Kushida Tetsuyuki Takahama

Citations