JACIII Vol.13 No.1 pp. 16-24
doi: 10.20965/jaciii.2009.p0016


Genetic Network Programming with Rules

Fengming Ye, Lu Yu, Shingo Mabu, Kaoru Shimada,
and Kotaro Hirasawa

2-7 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 808-0135, Japan

January 9, 2008
July 23, 2008
January 20, 2009
GNP, GNP with rules, exploration, exploitation
Genetic Network Programming (GNP) is an evolutionary approach which can evolve itself and find the optimal solutions. It is based on the idea of Genetic Algorithm and uses the data structure of directed graphs. Many papers have demonstrated that GNP can deal with complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is being used in many different areas such as data mining, extracting trading rules of stock markets, elevator systems, etc and GNP has obtained some outstanding results. In order to improve GNP's performance further, this paper proposes a new method called GNP with Rules. The aim of the proposed method is to balance exploitation and exploration of GNP, that is, to strengthen exploitation ability by using the exploited information extensively during the evolution process of GNP. The proposed method consists of 4 steps: rule extraction, rule selection, individual reconstruction and individual replacement. These 4 steps are added to the conventional algorithm of GNP. In order to measure the performance of the proposed method, the tileworld was used as the simulation environment. The simulation results show some advantages of GNP with Rules over conventional GNPs.
Cite this article as:
F. Ye, L. Yu, S. Mabu, K. Shimada, and K. Hirasawa, “Genetic Network Programming with Rules,” J. Adv. Comput. Intell. Intell. Inform., Vol.13 No.1, pp. 16-24, 2009.
Data files:
  1. [1] J. H. Holland, “Adaptation in Natural and Artificial Systems,” University of Michigan Press, Ann Arbor, 1975.
  2. [2] J. R. Koza, “Genetic Programming, on the Programming of Computers by Means of Natural Selection,” MIT Press, Cambridge, MA, 1992.
  3. [3] J. R. Koza, “Genetic Programming II, Automatic Discovery of Reusable Programs,” MIT Press, Cambridge, MA, 1994.
  4. [4] D. B. Fogel, “An introduction to simulated evolutionary optimization,” IEEE Transactions on Neural Networks, Vol.5, No.1, pp. 3-14, 1994.
  5. [5] S. Mabu, K. Hirasawa and J. Hu, “A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its extension Using Reinforcement Learning,” Evolutionary Computation, Vol.15, No.3, pp. 369-398, 2007.
  6. [6] K. Hirasawa, M. Okubo, H. Katagiri, J. Hu, and J. Murata. “Comparison between genetic network programming (GNP) and genetic programming (GP),” Proc. of Congress on Evolutionary Computation, pp. 1276-1282, 2001.
  7. [7] M. E. Pollack and M. Ringuette. “Introducing the tile-world: Experimentally evaluating agent architectures,” T. Dietterich, and W. Swartout (Eds.), Proc. of the conf. of the American Association for Artificial Intelligence, pp. 183-189, AAAI Press, 1990.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024