Volume 9, Issue 2 e1251
Overview

Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective

Adriano S. Koshiyama

Adriano S. Koshiyama

Department of Computer Science, University College London (UCL), London, UK

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Ricardo Tanscheit

Ricardo Tanscheit

Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil

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Marley M. B. R. Vellasco

Corresponding Author

Marley M. B. R. Vellasco

Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil

Correspondence

Marley M. B. R. Vellasco, Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, RJ 22451-000, Brazil.

Email: [email protected]

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First published: 05 March 2018
Citations: 5
Funding information FAPERJ; CNPq

Abstract

Studies in Evolutionary Fuzzy Systems (EFSs) began in the 90s and have experienced a fast development since then, with applications to areas such as pattern recognition, curve-fitting and regression, forecasting and control. An EFS results from the combination of a Fuzzy Inference System (FIS) with an Evolutionary Algorithm (EA). This relationship can be established for multiple purposes: fine-tuning of FIS's parameters, selection of fuzzy rules, learning a rule base or membership functions from scratch, and so forth. Each facet of this relationship creates a strand in the literature, as membership function fine-tuning, fuzzy rule-based learning, and so forth and the purpose here is to outline some of what has been done in each aspect. Special focus is given to Genetic Programming-based EFSs by providing a taxonomy of the main architectures available, as well as by pointing out the gaps that still prevail in the literature. The concluding remarks address some further topics of current research and trends, such as interpretability analysis, multiobjective optimization, and synthesis of a FIS through Evolving methods.

This article is categorized under:

  • Technologies > Computational Intelligence
  • Technologies > Machine Learning

Graphical Abstract

Generic Diagram of an Evolutionary Fuzzy System.

CONFLICT OF INTEREST

The authors have declared no conflicts of interest for this article.