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a review on the interpretability-accuracy trade-off in evolutionary multi-objective fuzzy systems (emofs)回顾interpretability-accuracy权衡进化多目标模糊系统(emof).pdf

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a review on the interpretability-accuracy trade-off in evolutionary multi-objective fuzzy systems (emofs)回顾interpretability-accuracy权衡进化多目标模糊系统(emof)

Information 2012, 3, 256-277; doi:10.3390/info3030256 OPEN ACCESS information ISSN 2078-2489 /journal/information Review A Review on the Interpretability-Accuracy Trade-Off in Evolutionary Multi-Objective Fuzzy Systems (EMOFS) Praveen Kumar Shukla 1,* and Surya Prakash Tripathi 2 1 Department of Information Technology, Babu Banarasi Das Northern India Institute of Technology, Lucknow 226001, India 2 Department of Computer Science Engineering, Institute of Engineering Technology, Lucknow 226001, India; E-Mail: tripathee_sp@yahoo.co.in * Author to whom correspondence should be addressed; E-Mail: praveenshuklak@; Tel.: +91-993-625-9191; Fax: +91-0522-391-1047. Received: 16 June 2012; in revised form: 21 June 2012 / Accepted: 29 June 2012 / Published: 12 July 2012 Abstract: Interpretability and accuracy are two important features of fuzzy systems which are conflicting in their nature. One can be improved at the cost of the other and this situation is identified as “Interpretability-Accuracy Trade-Off”. To deal with this trade-off Multi-Objective Evolutionary Algorithms (MOEA) are frequently applied in the design of fuzzy systems. Several novel MOEA have been proposed and invented for this purpose, more specifically, Non-Dominated Sorting Genetic Algorithms (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Fuzzy Genetics-Based Machine Learning (FGBML), (2 + 2) Pareto Archived Evolutionary Strategy ((2 + 2) PAES), (2 + 2) Memetic- Pareto Archived Evolutionary Strategy ((2 + 2) M-PAES), etc. This paper