ABSTRACT
This chapter considers the application of genetic algorithms as a general methodology for automatically generating fuzzy process controllers. In contrast to prior genetic-fuzzy systems which require that every input-output combination be enumerated, it proposes a novel encoding scheme which maintains only those rules necessary to control the target system. Genetic algorithms are probabilistic search techniques that emulate the mechanics of evolution. They are capable of globally exploring a solution space, pursuing potentially fruitful paths while also examining random points to reduce the likelihood of settling for a local optimum. A central issue in fuzzy control is the selection and encoding of the fuzzy rules. Sufficient system information must be encoded and the representation must be amenable to evaluation and, in the genetic framework, reproduction. The fuzzy controllers evolved by our method, like most systems generated using genetic algorithms, are very sensitive to the selection pressures imposed by the system designer.
