NDLI: Multi-objective evolutionary design of fuzzy rule-based systems

Content Provider	IEEE Xplore Digital Library
Author	Ishibuchi, H. Yamamoto, T.
Copyright Year	2004
Description	Author affiliation: Dept. of Industrial Eng., Osaka Prefecture Univ., Sakai, Japan (Ishibuchi, H.; Yamamoto, T.)
Abstract	This paper clearly demonstrates advantages of our evolutionary multiobjective optimization approach to the design of fuzzy rule-based classification systems over single-objective methods. The main advantage of our approach is that a large number of tradeoff (i.e., nondominated) fuzzy rule-based systems can be obtained by its single run with respect to conflicting objectives: accuracy maximization and complexity minimization. By analyzing the obtained fuzzy rule-based systems, the decision maker can understand the tradeoff between these two objectives. Such understanding is of great help when the decision maker chooses a final compromise fuzzy rule-based system. In the case of single-objective methods, only a single fuzzy rule-based system is obtained based on the pre-specified preference of the decision maker. We compare four formulations of genetic algorithm-based rule selection through computational experiments on well-known benchmark data sets. The four formulations have two objectives, their weighted sum, three objectives, and their weighted sum, respectively.
Starting Page	2362
Ending Page	2367
File Size	391735
Page Count	6
File Format	PDF
ISBN	0780385667
ISSN	1062922X
DOI	10.1109/ICSMC.2004.1400682
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-10-10
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy systems Knowledge based systems Fuzzy sets Industrial engineering Design optimization Genetic algorithms Evolutionary computation Neural networks Degradation Machine learning
Content Type	Text
Resource Type	Article

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