Sleep learning and Max-Min aggregation of Evolving Connectionist Systems
Michael John Watts · 2016
This paper describes two new algorithms for optimising the structure of trained Evolving Connectionist System (ECoS) artificial neural networks (ANN). It also presents the results of preliminary empirical evaluations of the algorithms. While ECoS are fast and efficient constructive ANN algorithms they can lose efficiency if they are allowed to grow too large. The algorithms presented in this paper reduce the size of a trained ECoS while retaining the knowledge that the ECoS has learned. That is, they remove redundant elements of the ECoS structure in such a way that the performance of the network is not reduced. The experimental evaluations showed that each algorithm is capable of achieving this to a different degree over different data sets. Optimisation of the parameters of one of the algorithms using an evolutionary algorithm yielded better results. While the work reported in this paper is preliminary, the results are promising and the algorithms have the potential to enhance the usefulness of ECoS ANN.