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Evolutionary algorithms for real-time artificial neural network training.

Jagadeesan, Ananda; Maxwell, Grant; MacLeod, Christopher


Ananda Jagadeesan

Grant Maxwell

Christopher MacLeod


Włodzisław Duch

Janusz Kacprzyk

Erkki Oja

Sławomir Zadrożny


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.

Start Date Sep 11, 2005
Publication Date Sep 1, 2005
Print ISSN 0302-9743
Publisher Springer (part of Springer Nature)
Pages 73-78
Series Title Lecture notes in computer science
Series Number 3697
Series ISSN 0302-9743
ISBN 9783540287551
Institution 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:
Keywords Evolutionary algorithms; Artificial neural networks; Robotics


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