A genetic deep learning model for electrophysiological soft robotics

Conference paper

Pandey, H. and Windridge, D. 2021. A genetic deep learning model for electrophysiological soft robotics. Balas, V., Jain, L., Balas, M. and Shahbazova, S. (ed.) 8th International Workshop on Soft Computing Applications. Arad, Romania 13 - 15 Sep 2018 Cham Springer. https://doi.org/10.1007/978-3-030-51992-6_12
TypeConference paper
TitleA genetic deep learning model for electrophysiological soft robotics
AuthorsPandey, H. and Windridge, D.
Abstract

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.

KeywordsDeep learning; Evolutionary algorithm; Genetic algorithm; Meta-heuristics; Neural networks
Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
Research GroupArtificial Intelligence group
Conference8th International Workshop on Soft Computing Applications
Proceedings TitleSoft Computing Applications: Proceedings of the 8th International Workshop Soft Computing Applications (SOFA 2018), Vol. I
SeriesAdvances in Intelligent Systems and Computing
EditorsBalas, V., Jain, L., Balas, M. and Shahbazova, S.
ISSN2194-5357
Electronic2194-5365
ISBN
Paperback9783030519919
Electronic9783030519926
PublisherSpringer
Place of publicationCham
Publication dates
Online14 Aug 2020
Print15 Aug 2020
Publication process dates
Submitted02 May 2018
Accepted25 Jun 2018
Deposited29 Jun 2018
Output statusPublished
Accepted author manuscript
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-51992-6_12
Web address (URL) of conference proceedingshttps://doi.org/10.1007/978-3-030-51992-6
LanguageEnglish
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