Variable Hidden Layer Sizing in Elman Recurrent Neuro-Evolution

摘要

The relationship between the size of the hidden layer in a neural network and performance in a particular domain is currently an open research issue. Often, the number of neurons in the hidden layer is chosen empirically and subsequently fixed for the training of the network. Fixing the size of the hidden layer limits an inherent strength of neural networks—the ability to generalize experiences from one situation to another, to adapt to new situations, and to overcome the “brittleness” often associated with traditional artificial intelligence techniques. This paper proposes an evolutionary algorithm to search for network sizes along with weights and connections between neurons.