A group search optimizer for neural network training

Authors:
S. He;Q. H. Wu;J. R. Saunders
Affiliations:
Department of Electrical Engineering and Electronics;Department of Electrical Engineering and Electronics;School of Biological Sciences, The University of Liverpool, Liverpool, UK
Venue:
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
Year:
2006

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Abstract

A novel optimization algorithm: Group Search Optimizer (GSO) [1] has been successfully developed, which is inspired by animal behavioural ecology. The algorithm is based on a Producer-Scrounger model of animal behaviour, which assumes group members search either for ‘finding' (producer) or for ‘joining' (scrounger) opportunities. Animal scanning mechanisms (e.g., vision) are incorporated to develop the algorithm. In this paper, we apply the GSO to Artificial Neural Network (ANN) training to further investigate its applicability to real-world problems. The parameters of a 3-layer feed-forward ANN, including connection weights and bias are tuned by the GSO algorithm. Two real-world classification problems have been employed as benchmark problems trained by the ANN, to assess the performance of the GSO-trained ANN (GSOANN). In comparison with other sophisticated machine learning techniques proposed for ANN training in recent years, including some ANN ensembles, GSOANN has a better convergence and generalization performances on the two benchmark problems.