A Genetic Deep Learning Model for Electrophysiological Soft Robotics

Pandey, Hari and Windridge, David (2018) A Genetic Deep Learning Model for Electrophysiological Soft Robotics. 8th International Workshop on Soft Computing Applications, 13/09/2018-15/09/2018, Arad, Romania, ISSN 2194-5357. (In Press)

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Deep learning methods are modeled by means of multiple layers of predefined set of operations. These days, deep learning techniques utilizing unsupervised 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.

Item Type: Conference or Workshop Item (Proceedings)
Uncontrolled Keywords: Deep learning, Evolutionary algorithm, Genetic algorithm, Metaheuristics,Neural networks
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computing and Information Systems
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Date Deposited: 13 Sep 2018 15:33
URI: http://repository.edgehill.ac.uk/id/eprint/10625

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