Fuzzy Rule-based Controllers That Learn by Evolving Their Knowledge Base. ?

Fuzzy Logic Controllers may be considered as knowledge-based systems , incorporating human knowledge into their Knowledge Base through Fuzzy Rules and Fuzzy Membership Functions. The deenition of these Fuzzy Rules and Fuzzy Membership Functions is actually aaected by subjective decisions, having a great innuence over the performance of the Fuzzy Controller. In recent years, eeorts have been made to obtain an improvement on system performance by incorporating learning mechanisms to modify predeened rules and/or membership functions. Genetic Algorithms are probabilistic search and optimization procedures based on natural genetics. This chapter describes two diierent approaches to apply Genetic Algorithms to Fuzzy Logic Controllers whose Rule Base is de-ned through a set of Fuzzy Rules. The use of a set of Fuzzy Rules (and not a Fuzzy Relational Matrix or a Fuzzy Decision table) is adapted to complex control applications containing a large number of variables, since it reduces the dimensionality of the Knowledge Base for these systems. The rst approach uses the Knowledge Base (containing a set of Fuzzy Rules and a set of Membership Functions) as the individual of the genetic system, working with a population of Fuzzy Controllers. The second one uses the Knowledge Base of the controller as the population of the genetic system (a single rule containing the description of the corresponding Fuzzy Sets is an individual of the population). Both systems have been applied to complex control problems (gait synthesis for biped robots and fossil power plants). Application examples cover oo-line and on-line optimization, and knowledge diversiication problems.