Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases

By Oscar Cordón, Francisco Herrera, Frank Hoffmann, Luis Magdalena. Advances in Fuzzy Systems - Applications and Theory, 2002

557 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/895d1e24e6bd1de8b6d399aeb2cc56e302d139b3

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In Artificial Intelligence

Citations in the corpus

Genetic fuzzy systems: taxonomy, current research trends and prospects (2008, background): “One of the most popular approaches is the hybridization between fuzzy logic and GAs leading to genetic fuzzy systems (GFSs) [27].”
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].”
Ten years of genetic fuzzy systems: current framework and new trends (2001, background): “Analysis of the literature shows that the most prominent types of GFSs are genetic fuzzy rulebased systems (GFRBSs) [33], whose genetic process learns or tunes di+erent components of a fuzzy rule-based system (FRBS).”
KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework (2011, background): “They have proven to be an important technique both for learning and knowledge extraction, making them a promising technique in DM [8] [16] [22] [24] [35] [46].”
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].”
A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, unknown): “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.”
KEEL: a software tool to assess evolutionary algorithms for data mining problems (2008, methodology): “This makes them also a promising tool in Data Mining (DM) [8,14,16,18, 35,56].”
Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base (2001, methodology): “Different proposals that use GAs in order to design FRBS are contained in [9], [20].”
Survey of computational intelligence as basis to big flood management: challenges, research directions and future work (2018, background): “Operationally speaking, GF systems are fuzzy systems that have the capability of learning from genetic algorithms (Cordón, Herrera, Hoffmann, & Magdalena, 2001).”
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].”