A comparison of some methods for evolving neural networks
Marko Grönroos · 1999
This paper presents an empirical comparison of four evolutionary encoding methods for finding suitable neural network topologies. We use five different learning problems for benchmarking the encoding methods. Three of the problems are artificial (Encoder and two function approximation problems), and two are real-world classification problems from the Proben1 benchmarking problem set. We train the network weights with a separate neural learning algorithm. Our evaluation criteria are classification accuracy and efficiency for using only the relevant variables.