Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base

By Oscar Cordón, Francisco Herrera, P. Villar. IEEE transactions on fuzzy systems, 2001

314 citations recorded by the source corpus.

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A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, unknown): “Themethod in [52] encodes the fuzzy partitions’ granularity and the definition parameters for each triangular-shapedmembership function in each fuzzypartitionwhile that in [50] learns the variables’ domain, the fuzzy partitions’ granularity, and the non-linear scaling functions to define their conte”
A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets (2008, methodology): “Specifically, we are currently studying two approaches: a Hierarchical System of Linguistic Rules Learning Methodology [9] and the generation of the Knowledge Base by the Genetic Learning of the Data Base [8].”
Ten years of genetic fuzzy systems: current framework and new trends (2001, methodology): “A priori genetic DB learning refers to a KB learning process in which a GA adapts the DB components such as scaling functions, membership functions and granularity parameters, whilst an additional fuzzy rule generation method derives the RB from the DB de5nition encoded in the chromosome [34,35,48,7”
Fuzzy human motion analysis: A review (2014, methodology): “[194] presented an approach to automatically learn the fuzzy rules by incorporating the genetic algorithm.”