  Fuzzy systems may be considered as knowledge-based systems that incorporates
human knowledge into their knowledge base through fuzzy rules and fuzzy
membership functions. The intent of this study is to present a fuzzy knowledge
integration framework using a Novel Evolutionary Strategy (NES), which can
simultaneously integrate multiple fuzzy rule sets and their membership function
sets. The proposed approach consists of two phases: fuzzy knowledge encoding
and fuzzy knowledge integration. Four application domains, the hepatitis
diagnosis, the sugarcane breeding prediction, Iris plants classification, and
Tic-tac-toe endgame were used to show the performance ofthe proposed knowledge
approach. Results show that the fuzzy knowledge base derived using our approach
performs better than Genetic Algorithm based approach.
