Optimization of an evolutionary algorithm for a tactile communication system

In this paper, we present an optimization method for a learning algorithm for generation of tactile stimuli which are adapted by means of tactile perception of a human. Because of special requirements for tactile perception tuning the optimization of the proposed learning algorithm cannot be performed basing on gradient-descent or likelihood estimation methods. Therefore, an automatic tactile classification (ATC) is introduced for the optimization process. The results show that the ATC equals the tactile comparison of humans and that the learning algorithm is successfully optimized by means of the developed ATC.