relation: https://khub.utp.edu.my/scholars/14712/
title: A GA approach to Optimization of Convolution Neural Network
creator: Naulia, P.S.
creator: Watada, J.
creator: Aziz, I.B.A.
creator: Roy, A.
description: In recent days a lot of activities in Deep Learning demonstrated ability to produce much better than other Machine Learning techniques. Much of the challenge in the Deep Learning is about optimizing the weights and several hyper parameters as it takes lot of computation and time to do. Gradient descent has been most popular technique currently in its weights optimization for back propagation. Most of the existing implementation of Convolution Neural Networks/Deep Learning Networks plays pivotal role in image processing. Though being scientifically regressive, BP and GD is slowly converging and getting easily trapped in local minima these are inherent disadvantages. For this reason, we explored another optimization with Meta Heuristic Algorithms such as Genetic Algorithm in the Deep Learning algorithm. © 2021 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2021
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Naulia, P.S. and Watada, J. and Aziz, I.B.A. and Roy, A.  (2021) A GA approach to Optimization of Convolution Neural Network.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112411080&doi=10.1109%2fICCOINS49721.2021.9497147&partnerID=40&md5=96dcbde660cad3083340b0c72fd84a4b
relation: 10.1109/ICCOINS49721.2021.9497147
identifier: 10.1109/ICCOINS49721.2021.9497147