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


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

August 14, 2005
January 6, 2006
July 20, 2006
evolutionary computation, genetic network programming (GNP), functional localization
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:
S. Eto, H. Hatakeyama, S. Mabu, K. Hirasawa, and J. 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.
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