Evolutionary Fuzzy Systems
Nazmul Haque Siddique, Hojjat Adeli · 2013
Although fuzzy systems have been applied successfully to many complex industrial processes, they experience a deficiency in knowledge acquisition and rely to a great extent on empirical and heuristic knowledge, which, in many cases, cannot be elicited objectively. One of the most important considerations in designing fuzzy systems is the construction of the membership functions for each linguistic variable, as well as the rule base. Empirical methods like trial-and-error are common practice in evolutionary computing, which is a time-consuming task. Therefore, a systematic approach to the choice of EA parameter values is very demanding. The two issues discussed lead to two synergistic combinations of fuzzy systems and evolutionary algorithms: (i) Evolutionary adaptive fuzzy systems, (ii) Fuzzy adaptive evolutionary algorithms. The objective of an EA adaptive fuzzy system is to adapt knowledge in fuzzy system design.