Comments on "constraining the optimization of a fuzzy logic controller"

M. D. Wu, Chuen-Tsai Sun

Research output: Contribution to journalLetter

2 Scopus citations


Genetic algorithms (GAs) are a highly effective and efficient means of solving optimization problems. Gene encoding, fitness landscape and genetic operations are vital to successfully developing a GA. Cheong and Lai1 described a novel method, which employed an enhanced genetic algorithm with multiple populations, to optimize a fuzzy controller, and the experimental results revealed that their method was effective in producing a well-formed fuzzy rule-base. However, their encoding method and fitness function appear unnatural and inefficient. This study proposes an alternative method of concise genetic encoding and fitness design.

Original languageEnglish
Pages (from-to)663-666
Number of pages4
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Issue number4
StatePublished - 1 Aug 2001


  • Fuzzy modeling
  • Genetic algorithms
  • Polyploidy

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