Using fuzzy logic to optimize genetic algorithm performance

Abstract

This paper reviews current methods of integrating genetic algorithms with fuzzy logic control. A fuzzy logic controlled genetic algorithm (FLC-GA) is proposed where operator selection and parameter adjustment is carried out dynamically and automatically. The fuzzy logic controller facilitates this automated control by employing an associated rule base and inference engine. This decides, using feedback from the genetic algorithm, what control action to take and when to take it, providing optimal solutions within reasonable time limitations.