Ten years of genetic fuzzy systems: current framework and new trends

By Oscar Cordón, F. Gomide, Francisco Herrera, Frank Hoffmann, L. Magdalena. Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), 2001

966 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/70e077f2d413bec2be5074273802cbe790a2a5b9

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Citations in the corpus

Optimization of Interval Type-2 Fuzzy Logic Controllers for a Perturbed Autonomous Wheeled Mobile Robot Using Genetic Algorithms (2009, methodology): “Analysis of the literature shows that the most prominent types of GFSs are genetic fuzzy rule-based systems (GFRBSs) [11], whose genetic process learns or tunes different components of a fuzzy rule-based system (FRBS).”
FURIA: an algorithm for unordered fuzzy rule induction (2009, methodology): “7 Both algorithms are frequently used for experimental purposes (e.g., (Fernández et al., 2007; Ishibuchi and Yamamoto, 2003; Cordon et al., 2004; Zolghadri and Mansoori, 2007)).”
Genetic fuzzy systems: taxonomy, current research trends and prospects (2008, background): “• This paper does not try to be a directory to guide the reader to a set of references, beginners in the topic can get three important list of references in [25, 27, 61].”
Learning Complexity-Bounded Rule-Based Classifiers by Combining. Association Analysis and Genetic Algorithms (2005, methodology): “Finally, our approach is of course related to more traditional genetic fuzzy systems [5].”
Evolving Fuzzy-Rule-Based Classifiers From Data Streams (2008, methodology): “where τ i denotes the firing level of (degree of confidence in) the ith fuzzy rule, which is determined as a product (a t-norm that represents the logical AND operator [2], [18]) of the membership values μj of the jth feature, j = [1, n], and the fuzzy set ( xj is around xi∗ j ) :”
A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, neutral): “The interested reader is referred to [53,54,85].”
Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, methodology): “Evolutionary techniques have been used to generate the rulebase of a fuzzy rule-based system, or fine-tune the membership functions, or both [61,62,127].”