JACIII Vol.9 No.4 pp. 430-436
doi: 10.20965/jaciii.2005.p0430


Genetic Network Programming for Automatic Program Generation

Shingo Mabu*, Kotaro Hirasawa*, Yuko Matsuya**, and Jinglu Hu*

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

**Graduate School of Information Science and Electrical Engineering, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan

September 23, 2004
February 11, 2005
July 20, 2005
evolutionary computation, genetic network programming, memory architecture, even-n-parity problem, mirror symmetry problem

In this paper, a recently proposed Evolutionary Computation method called Genetic Network Programming (GNP) is applied to generate programs such as Boolean functions. GNP is an extension of Genetic Algorithm (GA) and Genetic Programming (GP). It has a directed graph structure as gene and can search for solutions effectively. GNP has been mainly applied to dynamic problems and has shown better performances compared to GP. However, its application to static problems has not yet been studied well. Thus in this paper, GNP is applied to generate programs as its extension to solving static problems. In order to apply GNP to generating static problems, we introduced a new element, memory. In the proposed method, a GNP individual consists of a directed graph and a memory, while one in conventional GNP consists only of a directed graph. In the simulations, GNP succeeded in solving Even-n-Parity problem and Mirror Symmetry problem.

Cite this article as:
Shingo Mabu, Kotaro Hirasawa, Yuko Matsuya, and Jinglu Hu, “Genetic Network Programming for Automatic Program Generation,” J. Adv. Comput. Intell. Intell. Inform., Vol.9, No.4, pp. 430-436, 2005.
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Last updated on Mar. 01, 2021