Abstract
Analogous to biological evolution, cultural evolution also is a kind of optimal mechanism of nature. Studying this mechanism might possibly provide a more efficient computation for solving complicated problems, such as knowledge acquisition in large data set. In this paper, an algorithm, granular evolutionary algorithm for data classification, simply written as GEA, is proposed based on cultural evolution and granular computing. The proposed algorithm is essentially a granular computation, which is characterized by computing with granules. Each granule consists of some individuals, which itself also is an evolutionary population. The algorithm is realized in PVM environment by agent technology, and the experimental results certify its validity. Further analysis can find that the proposed algorithm has relatively better performance from large data sets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Zadeh, L.A.: Fuzzy Logic=Computing with Words. IEEE Transactions on Fuzzy Systems 4(1), 103–111 (1996)
Zadeh, L.A.: Towards a Theory of Fuzzy Information Granulation and its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Systems 19(1), 111–127 (1997)
Zadeh, L.A.: Some Reflections on Soft Computing, Granular Computing and their Roles in the Conception, Design and Utilization of Information/Intelligent Systems. Soft Computing 2(1), 23–25 (1998)
Pawlak, Z.: Rough Sets. International Journal of Information and Computer Science 11(5), 341–356 (1982)
Zheng, Z.: Tolerance Granular Space And Its Applications [Ph. D. dissertation]. Institute of Computing Technology, Chinese Academy of Sciences, Beijing (in Chinese) (2006)
Kryszkiewicz, M.: Rough Set Approach to Incomplete Information Systems. Information Sciences 112, 39–49 (1998)
Kryszkiewicz, M.: Rules in Incomplete Information Systems. Information Sciences 113, 271–292 (1999)
Leung, Y., Li, D.Y.: Maximal Consistent Block Technique for Rule Acquisition in Incomplete Information Systems. Information Sciences 153, 85–106 (2003)
Leung, Y., Wu, W.Z., Zhang, W.X.: Knowledge Acquisition in Incomplete Information Systems: A Rough Set Approach. European Journal of Operational Research 168, 164–180 (2006)
Meng, Z.Q., Cai, Z.X.: A New Computing: Granular Evolutionary Computing (in Chinese). Computer Engineering and application 42, 5–8 (2006)
Zhang, J., Li, X.W.: Evolutionary Granular Computing Model and Applications, Advances in Natural Computation. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 309–312. Springer, Heidelberg (2005)
Philip, G., Chase: The Emergence of Culture. In: The Evolution of a Uniquely Human Way of Life, Springer, New York (2006)
Henrich, J., Henrich, N.: Culture, Evolution and the Puzzle of Human Cooperation. Cognitive Systems Research 7, 220–245 (2006)
Sen, S., Knight, L., Legg, K.: Prototype based Supervised Concept Learning Using Genetic Algorithms. In: Dasgupta, D., Michalewicz, Z. (eds.) Evolutionary Algorithms in Engineering Applications, pp. 223–239. Springer, Heidelberg (1997)
Tan, K.C., Tay, A., Lee, T.H., et al.: Mining Multiple Comprehensible Classification Rules Using Genetic Programming. In: Proceedings of the 2002 Congress on Evolutionary Computation CEC 2002, pp. 1302–1307 (2002)
UCI Machine Learning Repository, http://www.ics.uci.edu/~mlearn/
Meng, Z.Q., Cai, Z.X.: A Method of Data Classification based on Parallel Genetic Algorithm (in Chinese). Computer Science 29(9s), 148–151 (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meng, Z., Shi, Z. (2007). A Granular Evolutionary Algorithm Based on Cultural Evolution. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-74581-5_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74580-8
Online ISBN: 978-3-540-74581-5
eBook Packages: Computer ScienceComputer Science (R0)