Multi - objective cooperative coevolution of neural networks for time series prediction

Chand, Shelvin and Chandra, Rohitash (2014) Multi - objective cooperative coevolution of neural networks for time series prediction. [Conference Proceedings]

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Abstract

The use of neural networks for time series prediction has been an important focus of recent research. Multi-objective optimization techniques have been used for training neural networks for time series prediction. Cooperative coevolution is an evolutionary computation method that decomposes the problem into subcomponents and has shown promising results for training neural networks. This paper presents a multi-objective cooperative coevolutionary method for training neural networks where the training data set is processed to obtain the different objectives for multi-objective evolutionary training of the neural network. We use different time lags as multi-objective criterion. The trained multi-objective neural network can give prediction of the original time series for preprocessed data sets distinguished by their time lags. The proposed method is able to outperform the conventional cooperative coevolutionary methods for training neural networks and also other methods from the literature on benchmark problems.

Item Type:	Conference Proceedings
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
Depositing User:	Rohitash Chandra
Date Deposited:	26 May 2014 21:14
Last Modified:	04 May 2016 21:27
URI:	http://repository.usp.ac.fj/id/eprint/7362

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