Multi - objective cooperative neuro - evolution of recurrent neural networks for time series prediction

Chandra, Rohitash (2015) Multi - objective cooperative neuro - evolution of recurrent neural networks for time series prediction. [Conference Proceedings]

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Abstract

Cooperative coevolution is an evolutionary computation method which solves a problem by decomposing it into smaller subcomponents. Multi-objective optimization deals with conflicting objectives and produces multiple optimal solutions instead of a single global optimal solution. In previous work, a multi-objective cooperative co-evolutionary method was introduced for training feedforward neural networks on time series problems. In this paper, the same method is used for training recurrent neural networks. The proposed approach is tested on time series problems in which the different time-lags represent the different objectives. Multiple pre-processed datasets distinguished by their time-lags are used for training and testing. This results in the discovery of a single neural network that can correctly give predictions for data pre-processed using different time-lags. The method is tested on several benchmark time series problems on which it gives a competitive performance in comparison to the methods in the literature.

Item Type:	Conference Proceedings
Additional Information:	DOI: 10.1109/CEC.2015.7256880
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:	10 Mar 2016 03:28
Last Modified:	11 Jul 2019 03:22
URI:	http://repository.usp.ac.fj/id/eprint/8433

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