Memetic cooperative neuro-evolution for chaotic time series prediction

Wong, Gary and Chandra, Rohitash and Sharma, Anuraganand (2016) Memetic cooperative neuro-evolution for chaotic time series prediction. [Conference Proceedings]

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Abstract

Cooperative neuro-evolution has shown to be promising for chaotic time series problem as it provides global search features using evolutionary algorithms. Back-propagation features gradient descent as a local search method that has the ability to give competing results. A synergy between the methods is needed in order to exploit their features and achieve better performance. Memetic algorithms incorporate local search methods for enhancing the balance between diversification and intensification. We present a memetic cooperative neuro-evolution method that features gradient descent for chaotic time series prediction. The results show that the proposed method utilizes lower computational costs while achieving higher prediction accuracy when compared to related methods. In comparison to related methods from the literature, the proposed method has favorable results for highly noisy and chaotic time series problems.

Item Type:	Conference Proceedings
Uncontrolled Keywords:	Memetic algorithms Cooperative neuro-evolution Backpropagation Gradient descent Feedforward networks
Subjects:	T Technology > TJ Mechanical engineering and machinery
Divisions:	Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
Depositing User:	Repo Editor
Date Deposited:	17 Oct 2016 22:41
Last Modified:	28 Mar 2022 23:48
URI:	https://repository.usp.ac.fj/id/eprint/9389

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