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Linguistic fuzzy modeling in high-dimensional regression problems poses the challenge of exponential-rule explosion when the number of variables and/or instances becomes high. One way to address this problem is by determining the used variables, the linguistic partitioning and the rule set together, in order to only evolve very simple, but still accurate models. However, evolving these components...
Recently evolutionary multiobjective optimization (EMO) algorithms have been frequently used for the design of fuzzy rule-based systems. Such a study is often referred to as multiobjective genetic fuzzy systems (MoGFS). Whereas a large number of interesting results on MoGFS have already been reported in the literature, the search ability of EMO algorithms in MoGFS is not necessarily high because fuzzy...
Multiobjective genetic fuzzy systems (MGFSs) have proved to be very effective in classification, regression and control tasks. However, large scale problems still present open and challenging research issues. Making identification of fuzzy rules faster can enlarge the range of applications of MGFSs. In this work we first analyze the time complexity for both the identification and the evaluation of...
Evolutionary algorithms have been successfully used in many studies to design accurate and interpretable fuzzy systems under the name of genetic fuzzy systems. Recently evolutionary multiobjective algorithms have been used for interpretability-accuracy tradeoff analysis of fuzzy systems. We first review a wide range of related studies to multiobjective genetic fuzzy systems. Then we illustrate multiobjective...
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