Evolutionary Intelligence

, Volume 1, Issue 1, pp 27–46 | Cite as

Genetic fuzzy systems: taxonomy, current research trends and prospects

  • Francisco HerreraEmail author
Review Article


The use of genetic algorithms for designing fuzzy systems provides them with the learning and adaptation capabilities and is called genetic fuzzy systems (GFSs). This topic has attracted considerable attention in the Computation Intelligence community in the last few years. This paper gives an overview of the field of GFSs, being organized in the following four parts: (a) a taxonomy proposal focused on the fuzzy system components involved in the genetic learning process; (b) a quick snapshot of the GFSs status paying attention to the pioneer GFSs contributions, showing the GFSs visibility at ISI Web of Science including the most cited papers and pointing out the milestones covered by the books and the special issues in the topic; (c) the current research lines together with a discussion on critical considerations of the recent developments; and (d) some potential future research directions.


Genetic fuzzy systems Fuzzy rule based systems Genetic algorithms Evolutionary algorithms Machine learning Data mining Computational Intelligence 



I would like to first thank to my colleagues Rafael Alcalá and Jesus Alcalá-Fdez who provided much helpful material for Sects. 4.4 and 3.1, respectively, and Alberto Fernández who mantains the paper’s web site. My sincere gratitude is also extended to Brian Carse, Jorge Casillas, Oscar Cordón, Pedro González, Hisao Ishibuchi, Francesco Marcelloni and Luciano Sánchez, for proofreading the manuscript and making helpful suggestions and corrections. This work was supported by the Spanish Ministry of Education and Science (MEC) under Project TIN2005-08386-C05-01.


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© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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