Osman Ibrahim, Ashraf and Shamsuddin, Siti Mariyam and Ahmad, Nor Bahiah and Qasem, Sultan Noman (2013) Multi objective genetic algorithm for training three term backpropagation network. In: 4th International Conference on Computing and Informatics (ICOCI 2013), 28 -30 August 2013, Kuching, Sarawak, Malaysia.

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Abstract

Multi Objective Evolutionary Algorithms has been applied for learning problem in Artificial Neural Networks to improve the generalization of the training and testing unseen data.This paper proposes the simultaneous optimization method for training Three Term Back Propagation Network (TTBPN) learning using Multi Objective Genetic Algorithm.The Non-dominated Sorting Genetic Algorithm II is applied to optimize the TTBPN structure by simultaneously reducing the error and complexity in terms of number of hidden nodes of the network for better accuracy in classification problem.This methodology is applied in two kinds of multiclasses data set obtained from the University of California at Irvine repository.The results obtained for training and testing on the datasets illustrate less network error and better classification accuracy, besides having simple architecture for the TTBPN.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	ISBN: 978983207879 Organized by: Universiti Utara Malaysia
Uncontrolled Keywords:	Artificial Neural Networks, Multi-objective evolutionary, Three Term Back Propagation, Non-dominated Sorting Genetic Algorithm II
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	College of Law, Government and International Studies
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	24 Aug 2014 01:39
Last Modified:	08 Apr 2015 02:05
URI:	https://repo.uum.edu.my/id/eprint/11966

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