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JACIII Vol.10 No.4 pp. 555-566
doi: 10.20965/jaciii.2006.p0555
(2006)

Paper:

Realizing Functional Localization Using Genetic Network Programming with Importance Index

Shinji Eto, Hiroyuki Hatakeyama, Shingo Mabu, Kotaro Hirasawa, and Jinglu Hu

Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan

Received:
August 14, 2005
Accepted:
January 6, 2006
Published:
July 20, 2006
Keywords:
evolutionary computation, genetic network programming (GNP), functional localization
Abstract

Many methods of generating behavior sequences of agents by evolution have been reported. A new evolutionary computation method named Genetic Network Programming (GNP) has also been developed recently along with these trends. The aim of this paper is to build an artificial model to realize functional localization of GNP considering the fact that the functional localization of the brain is realized in such a way that a different part of the brain corresponds to a different function. In this paper, it is especially stated that the switching function for functional localization can be realized using GNP with Importance Index (GNP IMX).

Cite this article as:
Shinji Eto, Hiroyuki Hatakeyama, Shingo Mabu, Kotaro Hirasawa, and Jinglu Hu, “Realizing Functional Localization Using Genetic Network Programming with Importance Index,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.4, pp. 555-566, 2006.
Data files:
References
  1. [1] R. A. Brooks, “Robust layered control system for a mobile robot,” IEEE Journal of Robotics and Automation, Vol.2, No.1, pp. 14-23, 1986.
  2. [2] B. L. M. Happel, and J. M. J. Murre, “Design and evolution of modular neural network architectures,” Neural Networks, Vol.7, pp. 985-1004, 1994.
  3. [3] Q. Xiong, K. Hirasawa, J. Hu, and J. Murata, “A functions localized neural network with branch gate,” Neural Networks, Vol.16, pp. 1461-1481, 2003.
  4. [4] H. Katagiri, K. Hirasawa, and J. Hu, “Genetic Network Programming –Application to Intelligent Agents–,” in Proc. of IEEE International Conference on System, Man and Cybernetics, pp. 3829-3834, 2000.
  5. [5] K. Hirasawa, M. Okubo, J. Hu, and J. Murata, “Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP),” in Proc. of IEEE CEC International Conference, pp. 1276-1282, 2001.
  6. [6] H. Katagiri, K. Hirasawa, J. Hu, and J. Murata, “Network Structure Oriented Evolutionary Model –Genetic Network Programming and Its Comparison with Genetic Programming–,” in Proc. of GECCO International Conference, pp. 219-226, 2001.
  7. [7] T. Eguchi, K. Hirasawa, J. Hu, and N. Ota, “A Study of Evolutionary Multiagent Models Based on Symbiosis,” IEEE Trans. on System, Man and Cybernetics –Part B–, Vol.35, No.1, 2006.
  8. [8] J. Holland, “Adaptation in Neural and Artificial Systems –An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence–,” Ann Arbor: University of Michigan Press, 1975.
  9. [9] J. P. Koza, “Genetic Programming,” Cambridge, MA: MIT Press, 1992.
  10. [10] R. Pfeifer, and C. Scheier, “Understanding Intelligence,” London, UK, 1999.
  11. [11] J. R. Koza, “Genetic Programming II: Automatic Discovery of Reusable Programs,” MIT Press, 1994.
  12. [12] A. Teller, and M. Veloso, “PADO : Leaning Tree-structured Algorithms for Orchestration into an Object Recognition System,” Carnegie Mellon University, Technical Report Library, 1995.

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