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JACIII Vol.16 No.7 pp. 851-863
doi: 10.20965/jaciii.2012.p0851
(2012)

Paper:

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An Explicit Memory Scheme of Genetic Network Programming

Shingo Mabu, Fengming Ye, and Kotaro Hirasawa

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

Received:
May 24, 2012
Accepted:
October 15, 2012
Published:
November 20, 2012
Creative Commons License
Keywords:
evolutionary computation, genetic network programming, directed graph, explicit memory
Abstract
Many classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Strategies (ES), etc. have made significant contribution to the study of evolutionary computation. And recently, a new approach named Genetic Network Programming (GNP) has been proposed especially for solving complex problems in dynamic environments. It is based on the algorithms of classical evolutionary computation techniques and uses data structures of directed graphs which are the unique feature of GNP. Focusing on GNP’s distinguished expression ability of the graph structure, this paper proposes an enhanced architecture for standard GNP in order to improve the performance of GNP by adopting an explicit memory scheme which records and utilizes the exploited information flexibly and extensively during the evolution process of GNP. In the enhanced architecture, the important gene information of the elite individuals is extracted and accumulated in the memory during evolution. Among the accumulated information, some of them are selected and used to guide the agents. In this paper, the proposed architecture is applied to the tileworld which is an excellent benchmark for evaluating the architecture demonstrating its superiority.
Cite this article as:
S. Mabu, F. Ye, and K. Hirasawa, “An Explicit Memory Scheme of Genetic Network Programming,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.7, pp. 851-863, 2012.
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