Application of Predictive Coding in Neuroevolution

Abstract

This paper presents promising results achieved by applying a new coding scheme based on predictive coding to neuroevolution. The technique proposed exploits the ability of a bit, which contains sufficient information, to represent its neighboring bits. In this way, a single bit represents not only its own information, but also that of its neighborhood. Moreover, whenever there is a change in bit representation, it is determined by a threshold value that determine the point at which the change in information is significant. The main contributions of this work are the following: (i) the ratio of the number of bits to the amount of information content is reduced; (ii) the complexity of the overall system is reduced as there is lesser amount of bit to process; (iii) Finally, we successfully apply the coding scheme to NEAT, which is used as a biometric classifier for the authentication of keystroke dynamics General TermsArtificial intelligence, Neural Network, Neuroevolution.

Keystroke Dynamics Based User Authentication using Deep Multilayer Perceptron

“…Numerous studies in keystroke dynamics have been proposed utilizing the statistical models to build the classifier [3]- [7], machine learning approach [8]- [12] and hybrid models [3], [13]- [15]. However, the model accuracy to differentiate typing pattern between genuine user and complexity of accessing multitude data are the significant challenges in those models [16].…”