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Multi-objective Evolutionary Fuzzy Systems

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Fuzzy Logic and Applications (WILF 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6857))

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Abstract

Several methods have been proposed to automatically generate fuzzy rule-based systems (FRBSs) from data. At the beginning, the unique objective of these methods was to maximize the accuracy with the result of often neglecting the most distinctive feature of the FRBSs, namely their interpretability. Thus, in the last years, the automatic generation of FRBSs from data has been handled as a multi-objective optimization problem, with accuracy and interpretability as objectives. Multi-objective evolutionary algorithms (MOEAs) have been so often used in this context that the FRBSs generated by exploiting MOEAs have been denoted as multi-objective evolutionary fuzzy systems. In this paper, we introduce a taxonomy of the different approaches which have been proposed in this framework. For each node of the taxonomy, we describe the relevant works pointing out the most interesting features. Finally, we highlight current trends and future directions.

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Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione: Elettronica, Informatica, Telecomunicazioni, University of Pisa, Largo Lucio Lazzarino 1, 56122, Pisa, Italy

    Pietro Ducange & Francesco Marcelloni

Authors
  1. Pietro Ducange
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  2. Francesco Marcelloni
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Editor information

Editors and Affiliations

  1. Computer Science Department, University of Bari, Via E. Orabona, 4, 70125, Bari, Italy

    Anna Maria Fanelli

  2. Department of Electrical and Computer Engineering, University of Alberta, T6G 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

  3. Department of Apllied Science, University of Naples Parthenope, Centro Direzionale Is. C4, 80143, Naples, Italy

    Alfredo Petrosino

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Ducange, P., Marcelloni, F. (2011). Multi-objective Evolutionary Fuzzy Systems. In: Fanelli, A.M., Pedrycz, W., Petrosino, A. (eds) Fuzzy Logic and Applications. WILF 2011. Lecture Notes in Computer Science(), vol 6857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23713-3_11

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  • DOI: https://doi.org/10.1007/978-3-642-23713-3_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23712-6

  • Online ISBN: 978-3-642-23713-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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