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Interpretability and accuracy are two important requirements during the development of fuzzy systems. This paper discusses various approaches related to the development of fuzzy systems in an Evolutionary Multiobjective Optimization (EMO) framework with good degree of interpretability and accuracy which are conflicting in their nature. This situation is well known as Interpretability-Accuracy (I-A) Trade-Off. Rule selection, rule learning, membership function tuning, fuzzy partition etc. are the major point of consideration to deal with this trade-off. Finally various recent issues related to this area in EMO framework are discussed.