Paper Info
Title
Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective
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.
Year
DOI
Venue
2019
10.1002/widm.1251
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
Keywords
Field
DocType
evolutionary fuzzy systems,fuzzy inference systems,genetic programming
Computer science,Genetic programming,Artificial intelligence,Fuzzy control system,Machine learning
Journal
Volume
Issue
ISSN
9.0
2.0
1942-4787
Citations 
PageRank 
References 
1
0.36
64
Authors
3
Name
Order
Citations
PageRank
Adriano Soares Koshiyama13410.19
Ricardo Tanscheit211821.53
Marley B. R. Vellasco328047.47