Abstract :
After almost twenty years of efforts towards augmenting fuzzy systems with learning and adaptation capabilities, one of the most prominent approaches to do so has resulted in the emergence of genetic fuzzy systems. These kinds of hybrid systems meld the approximate reasoning method of fuzzy systems with the adaptation capabilities of evolutionary algorithms. On the one hand, fuzzy systems have demonstrated the ability to formalize in a computationally efficient manner the approximate reasoning typical of humans. On the other hand, genetic (and in general evolution-inspired) algorithms constitute a robust technique in complex optimization, identification, learning, and adaptation problems. In this way, their confluence leads to increased capabilities for the design and optimization of fuzzy systems.
