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https://hdl.handle.net/2440/64238
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Type: | Conference paper |
Title: | Evolving fuzzy rules: evaluation of a new approach |
Author: | Ghandar, A. Michalewicz, Z. Neumann, F. |
Citation: | SEAL '10 Proceedings of 8th International Conference on Simulated Evolution and Learning (SEAL 2010), 2010: pp.250-259 |
Publisher: | Springer |
Publisher Place: | Germany |
Issue Date: | 2010 |
Series/Report no.: | Lecture Notes in Computer Science |
ISBN: | 3642172970 9783642172977 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | International Conference on Simulated Evolution and Learning (2010 : India) |
Editor: | Deb, K. Bhattacharya, A. Chakraborti, N. Chakroborty, P. Das, S. Dutta, J. Gupta, S.K. Jain, A. Aggarwal, V. Branke, J. Louis, S.J. Tan, K.C. |
Statement of Responsibility: | Adam Ghandar, Zbigniew Michalewicz and Frank Neumann |
Abstract: | Evolutionary algorithms have been successfully applied to optimize the rulebase of fuzzy systems. This has lead to powerful automated systems for financial applications. We experimentally evaluate the approach of learning fuzzy rules by evolutionary algorithms proposed by Kroeske et al. [10]. The results presented in this paper show that the optimization of fuzzy rules may be universally simplified regardless of the complex fitness surface for the overall optimization process. We incorporate a local search procedure that makes use of these theoretical results into an evolutionary algorithms for rule-base optimization. Our experimental results show that this improves a state of the art approach for financial applications. © 2010 Springer-Verlag. |
Rights: | Copyright 2010 Springer-Verlag Berlin, Heidelberg |
DOI: | 10.1007/978-3-642-17298-4_26 |
Grant ID: | http://purl.org/au-research/grants/arc/DP0985723 http://purl.org/au-research/grants/arc/DP0985723 |
Description (link): | http://portal.acm.org/citation.cfm?id=1947487 |
Published version: | http://dx.doi.org/10.1007/978-3-642-17298-4_26 |
Appears in Collections: | Aurora harvest Computer Science publications |
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