  Training of Artificial Neural Networks is a complex task of great importance
in supervised learning problems. Evolutionary Algorithms are widely used as
global optimization techniques and these approaches have been used for
Artificial Neural Networks to perform various tasks. An optimization algorithm,
called Group Search Optimizer (GSO), was proposed and inspired by the search
behaviour of animals. In this article we present two new hybrid approaches:
CGSO-Hk-WD and CGSO-Sk-WD. Cooperative GSOs are based on the divide-and-conquer
paradigm, employing cooperative behaviour between GSO groups to improve the
performance of the standard GSO. We also apply the weight decay strategy (WD,
acronym for Weight Decay) to increase the generalizability of the networks. The
results show that cooperative GSOs are able to achieve better performance than
traditional GSO for classification problems in benchmark datasets such as
Cancer, Diabetes, Ecoli and Glass datasets.
