A genetic deep learning model for electrophysiological soft robotics

Authors: Pandey, H.M. and Windridge, D.

Journal: Advances in Intelligent Systems and Computing

Volume: 1221 AISC

Pages: 145-151

eISSN: 2194-5365

ISSN: 2194-5357

DOI: 10.1007/978-3-030-51992-6_12


Deep learning methods are modelled by means of multiple layers of predefined set of operations. These days, deep learning techniques utilizing un-supervised learning for training neural networks layers have shown effective results in various fields. Genetic algorithms, by contrast, are search and optimization algorithm that mimic evolutionary process. Previous scientific literatures reveal that genetic algorithms have been successfully implemented for training three-layer neural networks. In this paper, we propose a novel genetic approach to evolving deep learning networks. The performance of the proposed method is evaluated in the context of an electrophysiological soft robot-like system, the results of which demonstrate that our proposed hybrid system is capable of effectively training a deep learning network.

Source: Scopus