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: | https://repository.usp.ac.fj/id/eprint/8433 |
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