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), <em>etc</em>. This paper introduces and reviews the approaches to the issue of developing fuzzy systems using Evolutionary Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off’ and mainly focusing on the work in the last decade. Different research issues and challenges are also discussed

Fuzzy knowledge-based systems (FKBS) are significantly applicable in the area of control, classification, and modeling, having knowledge in the form of fuzzy if-then rules. Type-2 fuzzy theory is used to make these systems more capable of dealing with inherent uncertainties in real-world problems. In this paper, the authors have proposed a genetic tuning approach named lateral displacement and expansion/compression (LDEC) in which α and β parameters are calculated to adjust the parameters of interval type-2 membership functions. α tuning deals with lateral displacement, whereas β tuning carries out compression/expansion operation. The interpretability and accuracy features are considered during the development of this approach. The experimental results show the performance of the proposed approach.

Fuzzy systems have remarkable capability to deal with imprecise and uncertain information existing in the real world complex problems. Evolutionary approaches, i.e., genetic algorithms are utilised to improvise the designing of fuzzy systems. During the design of fuzzy systems, interpretability and accuracy features are considered as an effort toward the improvement of performance and usability. One can only be improved at the cost of the other, leading to a new trade-off called interpretability-accuracy trade-off. On the other hand, the use of interval type-2 fuzzy sets in the development of fuzzy classifier is another dimension of improving it. In this paper, a fuzzy classifier named Engineering Student-Fuzzy Classification System (ES-FCS) is proposed and implemented using type-1 and type-2 fuzzy logic. The accuracy improvement has been studied by the application of linguistic hedges. The interpretability and accuracy assessment and their trade-off are experimentally studied in evolutionary multi-objective framework with both type-1 and type-2 fuzzy sets.

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