Analysis of the performance of a genetic algorithm-based system for message classification in noisy environments

Abstract

The process of knowledge acquisition must occur continually in those knowledge-based systems which must operate in noisy, contextually rich environments. One very important application with this requirement involves the inferring of the occurrence of events which cannot be exhaustively predefined from variably noisy sensor messages. Our paper describes on-going basic research for construction of an adaptive system which can perform high-level, rapid classification of sensor messages, possibly very noisy, concerning objects in its environment. The paper concentrates on experiments to determine optimal parameters for this bi-level, genetic algorithm-based system in low, medium, and high noise environments.