A novel approach to artificial neural network design using a combination of determined and stochastic optimization methods (the error backpropagation algorithm for weight optimization and the classical genetic algorithm for structure optimization) is described in this paper. The novel approach to GA-based structure optimization has a simplified solution representation that provides effective balance between the ANN structure representation flexibility and the problem dimensionality. The novel approach provides improvement of classification effectiveness in comparison with baseline approaches and requires less computational resource. Moreover, it has fewer parameters for tuning in comparison with the baseline ANN structure optimization approach. The novel approach is verified on the real problem of natural language call routing and shows effective results confirmed with statistical analysis.
['Roman B. Sergienko', 'Oleg Akhtiamov', 'Eugene Semenkin', 'Alexander Schmitt']
A Novel Approach to Neural Network Design for Natural Language Call Routing