An evolutionary method for the design of generic neural networks
D. Edwards, Keith Brown, Nicholas Kenelm Taylor · 2003
Hybrid systems using evolution to optimize neural network design or training are usually limited in scope and effectiveness. A system is presented that permits the widest variety of networks to be evolved using a two-stage GA approach. Networks generated for a benchmark machine learning task compare favourably with alternative methods.