Genetic fuzzy systems: taxonomy, current research trends and prospects

By Francisco Herrera. Evolutionary Intelligence, 2008

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A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, neutral): “One of the most successful fuzzy system identification methodologies within the realm of soft computing are genetic fuzzy systems (GFSs) [53] [54] [85] where genetic (and, in general, evolutionary) algorithms [63] are considered to learn the components of a FRBS.”
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures (2011, methodology): “in [65] present a hybrid genetic fuzzy system [32], to be applied to a bioaerosol detector problem.”
An overview on subgroup discovery: foundations and applications (2011, methodology): “The evolutionary algorithms proposed so far for the subgroup discovery task are based on the hybridisation between fuzzy logic and evolutionary algorithms, known as evolutionary fuzzy systems [34,57].”
A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning (2011, background): “The successful application of GAs to identify fuzzy systems has led to the so-called genetic fuzzy systems (GFSs) [36]–[38].”