On Integrating Fuzzy Knowledge Using a Novel Evolutionary Algorithm

TL;DR

This paper introduces a new evolutionary algorithm-based framework for integrating multiple fuzzy rule sets and membership functions, improving fuzzy knowledge bases across various applications.

Contribution

It presents a novel evolutionary strategy for simultaneous integration of fuzzy rule sets and membership functions, outperforming genetic algorithm methods.

Findings

Better performance than genetic algorithm-based approaches

Effective across multiple application domains

Enhances fuzzy knowledge base quality

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 ofthe proposed knowledge approach. Results show that the fuzzy knowledge base derived using our approach performs better than Genetic Algorithm based approach.