Abstract
In this paper, Rough Set Theory (RST) was introduced to discover knowledge hidden in the evolution process of Genetic Algorithm. Firstly it was used to analyze correlation between individual variables and their fitness function. Secondly, eigenvector was defined to judge the characteristic of the problem. And then the knowledge discovered was used to select evolution subspace and to realize knowledge-based evolution. Experiment results have shown that the proposed method has higher searching efficiency, faster convergent speed, and good performance for deceptive problem and multi-modal problems.
This paper is supported by the Youth Science Foundations Project of Shanxi Province (No.2006021016 and No.2007021018).
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Yan, G., Xie, G., Chen, Z., Xie, K. (2008). Knowledge-Based Genetic Algorithms. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2008. Lecture Notes in Computer Science(), vol 5009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79721-0_24
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DOI: https://doi.org/10.1007/978-3-540-79721-0_24
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