On reducing computational overhead in multi-objective genetic Takagi-Sugeno fuzzy systems

  • Authors:

  • Marco Cococcioni;Beatrice Lazzerini;Francesco Marcelloni

  • Affiliations:

  • Dipartimento di Ingegneria dell'Informazione: Elettronica, Informatica, Telecomunicazioni University of Pisa, Largo Lucio Lazzarino, 1, 56122 Pisa, Italy;Dipartimento di Ingegneria dell'Informazione: Elettronica, Informatica, Telecomunicazioni University of Pisa, Largo Lucio Lazzarino, 1, 56122 Pisa, Italy;Dipartimento di Ingegneria dell'Informazione: Elettronica, Informatica, Telecomunicazioni University of Pisa, Largo Lucio Lazzarino, 1, 56122 Pisa, Italy

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2011

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Abstract

The use of multi-objective evolutionary algorithms (MOEAs) to generate a set of fuzzy rule-based systems (FRBSs) with different trade-offs between complexity and accuracy has gained more and more interest in the scientific community. The evolutionary process requires, however, a large number of FRBS generations and evaluations. When we deal with high dimensional datasets, these tasks can be very time-consuming, especially when we generate Takagi-Sugeno FRBSs, thus making a satisfactory exploration of the search space very awkward. In this paper, we first analyze the time complexity for both the generation and the evaluation of Takagi-Sugeno FRBSs. Then we introduce a simple but effective technique for speeding up the identification of the rule consequent parameters, one of the most time-consuming phases in Takagi-Sugeno FRBS generation. Finally, we highlight how the application of this technique produces as a side-effect a decoupling of the rules. This decoupling allows us to avoid re-computing consequent parameters of rules which are not directly modified during the evolutionary process, thus saving a considerable amount of time. In the experimental part we first test the correctness of the predicted asymptotical time complexity. Then we show the benefits in terms of computing time saving and improved search space exploration through an example of multi-objective genetic learning of Takagi-Sugeno FRBSs in the regression domain.