On interpretation of graffiti commands for eBooks using a neural network and an improved genetic algorithm

Abstract

This paper presents the interpretation of graffiti commands for Electronic Books (eBooks). The interpretation process is achieved by training a proposed neural network (NN) with link switches using an improved genetic algorithm (GA). By introducing the switches to the links, the proposed NN can learn the optimal network structure automatically. The structure and the parameters of the NN are tuned by the improved GA, which is implemented by floating point numbers. The processing time of the improved GA is shorter as reflected by some benchmark test functions. Simulation results on interpreting graffiti commands for eBooks using the proposed NN with link switches and the improved GA will be shown.