Hasan, Shafaatunnur and Tan, Swee Quo and Shamsuddin, Siti Mariyam and Sallehuddin, Roselina (2011) Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm. In: 3rd International Conference on Computing and Informatics (ICOCI 2011), 8-9 June 2011, Bandung, Indonesia.

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Abstract

Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	ISBN 978-983-2078-49-4 Organized by: UUM College of Arts and Sciences, Universiti Utara Malaysia.
Uncontrolled Keywords:	Artificial Neural Network; Artificial Fish Swarm Algorithm; Classification problems.
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	College of Arts and Sciences
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	07 Apr 2015 07:00
Last Modified:	07 Apr 2015 07:00
URI:	https://repo.uum.edu.my/id/eprint/13633

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