On integrating fuzzy knowledge using a Novel Evolutionary Algorithm

Abstract

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 of the proposed knowledge approach. Results show that the fuzzy knowledge base derived using our approach performs better than genetic algorithm based approach.