Forming neural networks design through evolution

Neuroevolution techniques have been successful in many sequential decision tasks such as robot control and game playing. This paper aims at evolution in artificial neural networks (e.g. neuroevolution). Here is presented a neuroevolution system evolving populations of neurons that are combined to form the fully connected multilayer feedforward network with fixed architecture. In this paper, the transfer function has been shown to be an important part of architecture of the artificial neural network and have significant impact on an artificial neural network's performance. In order to test the efficiency of described method, we applied it to the alphabet coding problem.