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Evolutionary algorithms for real-time artificial neural network training.

Jagadeesan, Ananda; Maxwell, Grant; MacLeod, Christopher

Authors

Ananda Jagadeesan

Grant Maxwell

Christopher MacLeod



Contributors

W?odzis?aw Duch
Editor

Janusz Kacprzyk
Editor

Erkki Oja
Editor

S?awomir Zadro?ny
Editor

Abstract

This paper reports on experiments investigating the use of Evolutionary Algorithms to train Artificial Neural Networks in real time. A simulated legged mobile robot was used as a test bed in the experiments. Since the algorithm is designed to be used with a physical robot, the population size was one and the recombination operator was not used. The algorithm is therefore rather similar to the original Evolutionary Strategies concept. The idea is that such an algorithm could eventually be used to alter the locomotive performance of the robot on different terrain types. Results are presented showing the effect of various algorithm parameters on system performance.

Citation

JAGADEESAN, A., MAXWELL, G. and MACLEOD, C. 2005. Evolutionary algorithms for real-time artificial neural network training. In Duch, W., Kacprzyk, J., Oja, E. and Zadrozy, S (eds.) Artificial neural networks: formal models and their applications, part 2; proceedings of the 15th International on artificial neural networks (ICAAN 2005), 11-15 September 2005, Warsaw, Poland. Lecture notes in computer science, 3697. Berlin: Springer, pages 73-78. Available from: https://dx.doi.org/10.1007/11550907_12

Conference Name 15th International conference on artificial neural networks (ICAAN 2005): formal models and their applications
Conference Location Warsaw, Poland
Start Date Sep 11, 2005
End Date Sep 15, 2005
Acceptance Date Sep 11, 2005
Online Publication Date Dec 31, 2005
Publication Date Dec 31, 2005
Deposit Date Sep 16, 2016
Publicly Available Date Sep 16, 2016
Print ISSN 0302-9743
Publisher Springer
Pages 73-78
Series Title Lecture notes in computer science
Series Number 3697
Series ISSN 0302-9743
ISBN 9783540287551
DOI https://doi.org/10.1007/11550907_12
Keywords Evolutionary algorithms; Artificial neural networks; Robotics
Public URL http://hdl.handle.net/10059/1728

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