Encoding subcomponents in cooperative co-evolutionary recurrent neural networks

Authors:
Rohitash Chandra;Marcus Frean;Mengjie Zhang;Christian W. Omlin
Affiliations:
School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand and Department of Computing Science and Information Systems, Fiji National University, Suva, ...;School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand;School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand;Department of Computer Engineering, Northern Cyprus Campus, Middle East Technical University, Guzelyurt, KKTC, Mersin 10, Turkey
Venue:
Neurocomputing
Year:
2011

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Abstract

Cooperative coevolution employs evolutionary algorithms to solve a high-dimensional search problem by decomposing it into low-dimensional subcomponents. Efficient problem decomposition methods or encoding schemes group interacting variables into separate subcomponents in order to solve them separately where possible. It is important to find out which encoding schemes efficiently group subcomponents and the nature of the neural network training problem in terms of the degree of non-separability. This paper introduces a novel encoding scheme in cooperative coevolution for training recurrent neural networks. The method is tested on grammatical inference problems. The results show that the proposed encoding scheme achieves better performance when compared to a previous encoding scheme.