On reducing computational overhead in multi-objective genetic Takagi-Sugeno fuzzy systems
Applied Soft Computing
Expert Systems with Applications: An International Journal
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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 Takagi-Sugeno fuzzy rule-based systems. Then we introduce a simple but effective idea for fast identification of consequent parameters, although in an approximated, suboptimal manner. In the experimental part we first test the correctness of the predicted asymptotical time complexity. Then we show the benefits through an example of multiobjective genetic learning of compact and accurate fuzzy systems, in which we saved 71.3% of time on a 7 input problem.