Genetic Optimization of Fuzzy Rule-base System

A fuzzy rule-based system consists of fuzzy if-then rules such as “If x1 is small and x2 is small than y is large”. The problem with existing fuzzy rule-based systems is that the size of the rule-base (number of rules) increases exponentially with the increase of the number of fuzzy sets involved in the rules. This exponential increase in size of the rule-base increases the search time and hence the problem solving time, and also the memory space required. In this paper a fuzzy rule-base compaction using genetic algorithm is proposed. The proposed approach consists of three phases: Knowledge acquisition phase, Encoding phase, and Compaction phase or Optimization phase. In knowledge acquisition phase information from various knowledge sources i.e. experts and machine learning methods is integrated into a single knowledge base. In encoding phase rule set and corresponding membership functions from different knowledge sources is encoded into a variable length string or chromosomes so that they can contribute to the genetic optimization approach. In optimization phase genetic algorithm that results in an optimal or nearly optimal set of fuzzy rules and membership functions from the initial set of rules and membership function is proposed.