Evolution of neural networks using weight mapping
João Carlos Figueira Pujol, Riccardo Poli · 1999
The application of genetic programming to the evolution of neural networks has been hindered by the inadequacy of parse trees to represent oriented graphs, and by the lack of a good mechanism for encoding the weights. In this work, a hybrid method is introduced, where genetic programming evolves a mapping function to adapt the weights, whereas a genetic algorithm-based approach evolves the architecture. Results on the application of the new method to the evolution of feedforward and recurrent neural networks are reported. 1 Introduction The training of artificial neural networks for a particular task can be seen as a mapping of the initial set of random weights into a new set of adapted weights which solves the problem. That means, the process of training defines a function to map the initial set of random weights into the correct ones. For example, the backpropagation training algorithm [1] is an attempt to construct such a mapping function iteratively. All training algor...