Real-time acquisition of fuzzy rules using genetic algorithms

The paper presents a Genetic Algorithm(GA)-based system for online acquisition and modification of rules for a fuzzy logic controller. This uses a version of the rule competition production systems, called Classifier Systems, but in which the rules are matched in the fuzzy domain rather than as binary patterns. The GA is operated in the incremental mode whereby only one structure from a population is evaluated in each time interval. To hasten the learning process, the payoff received is used to assign estimates of new strengths to the other classifiers, dependent on the degree of matching with the evaluated classifier. The rule learning is initialized with randomly generated structures to which fairly general heuristic knowledge has been added. The interacting environment has been modelled by a real time simulation of closed loop administration of an anaesthetic drug, but the characteristics of the environment are not known to the GA.