Forming Neural Networks Design through Evolution

Eva Volná


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.


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Paper Citation

in Harvard Style

Volná E. (2007). Forming Neural Networks Design through Evolution . In Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2007) ISBN 978-972-8865-86-3, pages 13-20. DOI: 10.5220/0001624700130020

in Bibtex Style

author={Eva Volná},
title={Forming Neural Networks Design through Evolution},
booktitle={Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2007)},

in EndNote Style

JO - Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2007)
TI - Forming Neural Networks Design through Evolution
SN - 978-972-8865-86-3
AU - Volná E.
PY - 2007
SP - 13
EP - 20
DO - 10.5220/0001624700130020