A fuzzy logic controller with learning through the evolution of its knowledge base

Fuzzy logic controllers (FLCs) constitute knowledge-based systems that include fuzzy rules and fuzzy membership functions to incorporate human knowledge into, their knowledge base. The specification of fuzzy rules and fuzzy membership functions is one of the key question when designing FLCs, and is generally affected by subjective decisions. Some efforts have been made to obtain an improvement on system performance by incorporating learning mechanisms to modify the rules and/or membership functions of the FLC. Genetic algorithms are probabilistic search and optimization procedures based on natural genetics. This paper proposes a way to apply (with a learning purpose) genetic algorithms to FLCs, and presents an application designed to control the synthesis of the biped walk of a simulated 2-D biped robot. (C) 1997 Elsevier Science Inc.