Lean Artificial Neural Networks - Regularization Helps Evolution

A main criterion for the accuracy of solutions of an Artificial Neural Network (ANN) for classification tasks is the architecture. In order to find problem-adapted topologies of ANNs, we adopted the evolutionary approach to ANN design by employing a Genetic Algorithm (GA) to evolve ANNs which are represented using a direct encoding method. As ANNs of low complexity show better generalization capabilities than more complex networks, we incorporated a regularization term into the fitness function which together with the problem representation is determining the preferred regions of the search space the GA will focus on. Especially, we investigated two different regularization terms proposed in literature and experimented with various degrees of impact on the fitness function. The parallel netGEN system which has been implemented by the authors is generating problem-adapted Feed-Forward ANNs being trained by Error-Back-Propagation. Empirical results on a real world problem taken from the PROBEN1 ANN benchmark suite are presented.
Helmut A. Mayer <helmut@cosy.sbg.ac.at>
Last modified: Mon Jan 5 1998