Evolution and design of distributed learning rules
Thomas Philip Rúnarsson, Magnus Jönsson · 2002
The paper describes the application of neural networks as learning rules for the training of neural networks. The learning rule is part of the neural network architecture. As a result the learning rule is non-local and globally distributed within the network. The learning rules are evolved using an evolution strategy. The survival of a learning rule is based on its performance in training neural networks on a set of tasks. Training algorithms will be evolved for single layer artificial neural networks. Experimental results show that a learning rule of this type is very capable of generating an efficient training algorithm.