Neural network design methodology based on evolutionary learning

Nowadays, the theories, models, methods and tools concerning neural networks are approaching complete and mature. Nevertheless, there still exists a main difficulty for industrial applications. That is how to design optimal network architecture according to every specific problem. The task includes optimization of network size, network topology, connection weights between neurons etc. This paper proposes an automatic design methodology of neural networks based on evolutionary learning. We analyze firstly the building blocks of neural networks in order to obtain design specifications. Then, we develop a genetic encoding method, after which the evolution process is elaborated for finding the optimal neural network. The results of our experiments reveal that our methodology is superior to the error back-propagation algorithm both for its executing efficiency and performance.