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Cheng-Jian Lin and Yong-Ji Xu
Department of Computer Science and Information Engineering
Chaoyang University of Technology
Taichung, 413 Taiwan
E-mail: cjlin@mail.cyut.edu.tw
In this paper, a TSK-type fuzzy model (TFM) with a hybrid evolutionary learning
algorithm (HELA) is proposed. The proposed HELA method combines the compact genetic
algorithm (CGA) and the modified variable-length genetic algorithm (MVGA).
Both the number of fuzzy rules and the adjustable parameters in the TFM are designed
concurrently using the HELA method. In the proposed HELA method, individuals of the
same length constitute the same group, with multiple groups in a population. Moreover,
the proposed HELA method adopts the compact genetic algorithm (CGA) to carry out
the elite-based reproduction strategy. The CGA represents a population as a probability
distribution over the set of solutions and is operationally equivalent to the order-one behavior
of the simple GA. The evolution processes of a population consist of three major
operations: group reproduction using the compact genetic algorithm, variable two-part
individual crossover, and variable two-part mutation. Computer simulations have demonstrated
that the proposed HELA method performs better than some existing methods.
Received February 21, 2005; revised April 27, 2005; accepted June 8, 2005.
Communicated by Chin-Teng Lin.
* This research was supported in part by the National Science Council of Taiwan, R.O.C., under grant No.
NSC 95-2221-E-324-028.