Fast Learning In Multilayered Neural Networks By Means Of Hybrid Evolutionary And Gradient Algorithms

: This paper describes two algorithms based on cooperative evolution of internal hidden network representations and a combination of global evolutionary and local search procedures. The obtained experimental results are better in comparison with prototype methods. It is demonstrated, that the applications of pure gradient or pure genetic algorithms to the network training problem is much worse than hybrid procedures, which reasonably combine the advantages of global as well as local search. 1. INTRODUCTION Artificial Neural Networks (ANN) allows to approach effectively a large class of applications including pattern recognition, visual perception, signal processing and control systems. The most progress in this field is related to invention of the error backpropagation algorithm by Rumelhart et al. [1]. Backpropagation is now a conventional procedure for ANN training. However, the backpropagation as well as its numerous modifications, often leads to typical problems for gradient descen..