Evolutionary Multiobjective Optimization for Fuzzy Knowledge Extraction
نویسنده
چکیده
− A new trend in the design of fuzzy rulebased systems is the use of evolutionary multiobjective optimization (EMO) algorithms. This trend is observed in various areas in machine learning. EMO algorithms are often used to search for a number of Pareto-optimal non-linear systems with respect to their accuracy and complexity. In this paper, we first explain some basic concepts in multiobjective optimization and an outline of EMO algorithms. Next we explain the use of EMO algorithms to search for Pareto-optimal fuzzy rules. Then we explain their use to search for Pareto-optimal fuzzy systems. Finally we discuss future directions. Keywords− Fuzzy data mining, multiobjective design of fuzzy systems, evolutionary multiobjective optimization.
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