On Integrating Fuzzy Knowledge Using a Novel Evolutionary Algorithm
This work addresses knowledge integration in fuzzy systems for domains like medical diagnosis and agriculture, but it appears incremental as it builds on existing evolutionary methods.
The study tackled the problem of integrating multiple fuzzy rule sets and membership functions by proposing a fuzzy knowledge integration framework using a Novel Evolutionary Strategy (NES), resulting in better performance than a Genetic Algorithm-based approach across four application domains.
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.