A Effective Knowledge Integration Algorithm Based on Culture Algorithm Framework

Abstract:

The key to obtain an effective knowledge base to improve decision quality and enhance organizational core competency is integrating the knowledge from different subjects and sources. We put forward a knowledge integration strategy based on culture algorithm framework. It encodes the knowledge uniformly and evolutes among the two phases of population space and belief space. Through the communication protocol established among the two spaces, an effective and concise knowledge base is obtained. The experiment shows that comparing with traditional genetic algorithm the model can classify the knowledge more precisely, reduce redundant knowledge and remove contradictory knowledge.

• Periodical: Applied Mechanics and Materials (Volumes 26-28)
• Pages: 310-314
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.26-28.310
• Online since: June 2010
• Authors: Si Hua Chen
• Keywords: Cultural Algorithm, Genetic Algorithm (GA), Knowledge Coding, Knowledge Integration

References

[1] Grant R.M. Toward a knowledge-based theory of the firm [J]. Strategic Management, 1996, 11(7): 109-122.

[2] Volberda H.W. Toward the flexible form: How to remain vital in hyper competitive environments[J]. Organization Science, 1996, 7(4): 359-374.

DOI: 10.1287/orsc.7.4.359

[3] Kee Young Kwahk, Hee Woong Kim, Hock Chuan Chan. A Knowledge Integration Approach for Organizational Decision Support[J]. Journal of Database Management, 2007, 18(2): 41-61.

DOI: 10.4018/jdm.2007040103

[4] Xiangyang Li , Charu Chandra. A knowledge integration framework for complex network management[J]. Industrial Management & Data Systems, 2007, 107(8): 1089-1109.

DOI: 10.1108/02635570710822769

[5] Cordon O, Herrera F., Lozano M. On the combination of fuzzy logic and evolutionary computation: a short review and bibliography[J]. Fuzzy evolutionary computation, 1997, 20(10): 33-56.

DOI: 10.1007/978-1-4615-6135-4_2

[6] Wang C. H, Tseng S. S, Hong, T. P. Design of a self-adaptive brain tumor diagnostic system[J]. Journal of Information Science and Engineering, 1995, 11(2): 275-294.

[7] W. H. Durham. Co-evolution: Genes, Culture, and Human Diversity[M]. Stanford University Press. (1994).

[8] A. C. Renfrew. Dynamic modeling in archaeology: What, when, and where? In Dynamical Modeling and the Study of Change in Archaelogy[M]. Edinburgh University Press. (1994).

[9] R. G. Reynolds. An introduction to cultural algorithms [C]. In Proceedings of the Third Annual Conference on Evolutionary Programming, World Scientific, River Edge, New Jersey, 1994: 131-139.

[10] Meng Fan-rong, Hao Xiao-yun, Zhou Yong. Selective Neural Network Ensemble Approach Based on Cultural Algorithm[J]. Journal of Chinese Computer Systems，2009, 30(5): 933-936.

[11] Zhang Di, Yang Yan, Tang Rui-Xue. New hybrid clustering algorithm based on cultural algorithms[J]. Computer Engineering and Applications. 2009, 45(4): 159-161.