Competitive island cooperative neuro - evolution of feedforward networks for time series prediction

Nand, Ravneil and Chandra, Rohitash (2016) Competitive island cooperative neuro - evolution of feedforward networks for time series prediction. In: Artificial Life and Computational Intelligence. Lecture Notes in Computer Science, 9592 . Springer International Publishing, Switzerland, pp. 285-297. ISBN 9783319282695

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Abstract

Problem decomposition, is vital in employing cooperative coevolution for neuro-evolution. Different problem decomposition methods have features that can be exploited through competition and collaboration. Competitive island cooperative coevolution (CICC) implements decomposition methods as islands that compete and collaborate at different phases of evolution. They have been used for training recurrent neural networks for time series problems. In this paper, we apply CICC for training feedforward networks for time series problems and compare their performance. The results show that the proposed approach has improved the results when compared to standalone cooperative coevolution and shows competitive results when compared to related methods from the literature.

Item Type:	Book Chapter
Additional Information:	DOI:10.1007/978-3-319-28270-1_24
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:	Ravneil Nand
Date Deposited:	23 Aug 2016 02:13
Last Modified:	22 Mar 2017 02:46
URI:	https://repository.usp.ac.fj/id/eprint/9183

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