Title: Optimization of Fuzzy Systems Using Group-Based Evolutionary Algorithm
Authors: Chang, Jyh-Yeong
Han, Ming-Feng
Lin, Chin-Teng
Institute of Electrical and Control Engineering
Keywords: fuzzy system (FS);differential evolution (DE);group-based evolutionary algorithm (GEA);optimization
Issue Date: 1-Jan-2012
Abstract: This paper proposes a group-based evolutionary algorithm (GEA) for the fuzzy system (FS) optimization. Initially, we adopt an entropy measure method to determine the number of rules. Fuzzy rules are automatically generated from training data by entropy measure. Subsequently, the GEA is performed to optimize all the free parameters for the FS design. In the evolution process, a FS is coded as an individual. All individuals based on their performance are partitioned into a superior group and an inferior group. The superior group, which is composed of individuals with better performance, uses a global evolution operation to search potential individuals. In the inferior group, individuals with a worse performance employ the local evolution operation to search better individuals near the current best individual. Finally, the proposed FS with GEA model (FS-GEA) is applied to time series forecasting problem. Results show that the proposed FS-GEA model obtains better performance than other algorithm.
URI: http://hdl.handle.net/11536/124887
ISBN: 978-3-642-34487-9
ISSN: 0302-9743
Volume: 7665
Begin Page: 291
End Page: 298
Appears in Collections:Conferences Paper