Enhancing competitive island cooperative neuro - evolution through backpropagation for pattern classification

Wong , Gary and Chandra, Rohitash (2015) Enhancing competitive island cooperative neuro - evolution through backpropagation for pattern classification. In: Neural Information Processing. Lecture Notes in Computer Science . Springer International Publishing, Switzerland, pp. 293-301. ISBN Print 978-3-319-26531-5 online 978-3-319-26532-2

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Abstract

Cooperative coevolution is a promising method for training neural networks which is also known as cooperative neuro-evolution. Cooperative neuro-evolution has been used for pattern classification, time series prediction and global optimisation problems. In the past, competitive island based cooperative coevolution has been proposed that employed different instances of problem decomposition methods for competition. Neuro-evolution has limitations in terms of training time although they are known as global search methods. Backpropagation algorithm employs gradient descent which helps in faster convergence which is needed for neuro-evolution. Backpropagation suffers from premature convergence and its combination with neuro-evolution can help eliminate the weakness of both the approaches. In this paper, we propose a competitive island cooperative neuro-evolutionary method that takes advantage of the strengths of gradient descent and neuro-evolution. We use feedforward neural networks on benchmark pattern classification problems to evaluate the performance of the proposed algorithm. The results show improved performance when compared to related methods.

Item Type:	Book Chapter
Additional Information:	Doi: 10.1007/978-3-319-26532-2_32 Proceedings of the 22nd International Conference on Neural Information Processing. Paper presented at the International Conference on Neural Information Processing, 9-12 November 2015 Istanbul, Turkey.
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 03:58
Last Modified:	09 Jun 2016 21:48
URI:	http://repository.usp.ac.fj/id/eprint/8431

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