Cover Image

Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems

Nature-Inspired Computing (NIC) has always been a promising tool to enhance neural network learning. Artificial Fish Swarm Algorithm (AFSA) as one of the NIC methods is widely used for optimizing the global searching of ANN. In this study, we applied the AFSA method to improve the Multilayer Percept...

Full description

Saved in:
Bibliographic Details
Main Authors: Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina
Format: Article
Language:English
Published: Universiti Utara Malaysia Press 2012
Subjects:
QA75 Electronic computers. Computer science
Online Access:https://repo.uum.edu.my/id/eprint/30417/1/JICT%2011%2000%202012%2037-53.pdf
https://repo.uum.edu.my/id/eprint/30417/
https://www.e-journal.uum.edu.my/index.php/jict/article/view/8123
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uum.repo.30417
record_format eprints
spelling my.uum.repo.304172024-02-14T15:01:52Z https://repo.uum.edu.my/id/eprint/30417/ Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems Hasan, Shafaatunnur Tan, Swee Quo Shamsuddin, Siti Mariyam Sallehuddin, Roselina QA75 Electronic computers. Computer science Nature-Inspired Computing (NIC) has always been a promising tool to enhance neural network learning. Artificial Fish Swarm Algorithm (AFSA) as one of the NIC methods is widely used for optimizing the global searching of ANN. In this study, we applied the AFSA method to improve the Multilayer Perceptron (MLP) learning for promising accuracy in various classification problems. The parameters of AFSA: AFSA prey, AFSA swarm and AFSA follow are implemented on the MLP network for improving the accuracy of various classification datasets from UCI machine learning. The results are compared to other NIC methods, i.e., Particle Swarm Optimization (PSO) and Differential Evolution (DE), in which AFSA gives better accuracy with feasible performance for all datasets. Universiti Utara Malaysia Press 2012 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/30417/1/JICT%2011%2000%202012%2037-53.pdf Hasan, Shafaatunnur and Tan, Swee Quo and Shamsuddin, Siti Mariyam and Sallehuddin, Roselina (2012) Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems. Journal of Information and Communication Technology, 11. pp. 37-53. ISSN 2180-3862 https://www.e-journal.uum.edu.my/index.php/jict/article/view/8123 10.32890/jict 10.32890/jict 10.32890/jict
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutional Repository
url_provider http://repo.uum.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Hasan, Shafaatunnur
Tan, Swee Quo
Shamsuddin, Siti Mariyam
Sallehuddin, Roselina
Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems
description Nature-Inspired Computing (NIC) has always been a promising tool to enhance neural network learning. Artificial Fish Swarm Algorithm (AFSA) as one of the NIC methods is widely used for optimizing the global searching of ANN. In this study, we applied the AFSA method to improve the Multilayer Perceptron (MLP) learning for promising accuracy in various classification problems. The parameters of AFSA: AFSA prey, AFSA swarm and AFSA follow are implemented on the MLP network for improving the accuracy of various classification datasets from UCI machine learning. The results are compared to other NIC methods, i.e., Particle Swarm Optimization (PSO) and Differential Evolution (DE), in which AFSA gives better accuracy with feasible performance for all datasets.
format Article
author Hasan, Shafaatunnur
Tan, Swee Quo
Shamsuddin, Siti Mariyam
Sallehuddin, Roselina
author_facet Hasan, Shafaatunnur
Tan, Swee Quo
Shamsuddin, Siti Mariyam
Sallehuddin, Roselina
author_sort Hasan, Shafaatunnur
title Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems
title_short Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems
title_full Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems
title_fullStr Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems
title_full_unstemmed Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems
title_sort artificial fish swarm optmization for multilayernetwork learning in classification problems
publisher Universiti Utara Malaysia Press
publishDate 2012
url https://repo.uum.edu.my/id/eprint/30417/1/JICT%2011%2000%202012%2037-53.pdf
https://repo.uum.edu.my/id/eprint/30417/
https://www.e-journal.uum.edu.my/index.php/jict/article/view/8123
_version_ 1792158592355270656
score 13.211869

Similar Items