Special issue on "Genetic Fuzzy Systems: Recent Developments and Future Directions"

Fuzzy systems have demonstrated their ability to solve different kinds of problems in various application domains. In most cases, the key for success was their ability to incorporate human expert knowledge. In the 1990s, despite the previous palmy history, a certain interest for the study of fuzzy systems with added learning capabilities emerged. Two of the most important approaches have been the hybridization attempts made in the framework of soft computing, where different techniques such as neural networks and evolutionary computation provide fuzzy systems with learning ability. A Genetic Fuzzy System (GFS) is basically a fuzzy system, usually a fuzzy rule-based system (FRBS), augmented by a learning process based on evolutionary computation, which includes Genetic Algorithms, Genetic Programming, or Evolutionary Strategies, among others. Evolutionary learning processes cover different levels of complexity according to the structural changes produced in the fuzzy system by the algorithm. The fusion of these population-based, robust search algorithms with a representation that offers linguistic interpretability such as fuzzy systems provides a powerful paradigm for computational intelligence research. Among the main reasons to bet the use of evolutionary algorithms instead other optimization/learning techniques to design fuzzy systems we can highlight the following. First, they provide a powerful and flexible search capability (such as the use of multiple objectives, constrained objectives, or multimodal objectives) that allow them to address a wide range of problems. Second, they can process flexible representation structures (such as mixed coding schemes or constrained representation) that allow them to deal with almost any kind of fuzzy system. Besides, they can run on distributed and cellular architectures, perform incremental learning, and easily hybrid with other techniques for complex tasks. The field of GFS has now reached a stage of maturity after the earliest papers were published 17 years ago. Although the maturity of the GFS field means it is now being applied to an ever growing number of real-world applications, there are many basic issues yet to be resolved and there is an active and vibrant worldwide community of researchers working on these issues.