Abstract
In this paper a team of agents applied to train a feed-forward artificial neural networks is proposed, implemented and experimentally evaluated. The approach is based on a new variant of the A-Team architecture. Each agent member of the team is executing its own simple training procedure and it is expected that the whole team demonstrates complex collective behavior. The paper includes a description of the proposed approach and presents the results of the experiment involving benchmark datasets. The results show that the approach could be considered as a competitive training tool.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
W. Duch & J. Korczak. Optimization and Global Minimization Methods Suitable for Neural Network. Neural Computing Surveys 2, 1998, http://www.icsi.berkeley.edu/jagopta/CNS.
I. Czarnowski, P. Jedrzejowicz, E. Ratajczak. Population Learning Algorithm — Example Implementations and Experiments. Proceedings of the Fourth Metaheuristics International Conference, Porto, 2001, 607–612.
I. Czarnowski & P. Jedrzejowicz. Population Learning Metaheuristic for Neural Network Training. Proceedings of the Sixth International Conference on Neural Networks and Soft Computing (ICNNSC), Zakopane, Poland, 2002.
I. Czarnowski & P. Jedrzejowicz. Application of the Parallel Population Learning Algorithm to Training Feed-forward ANN. Proceedings of the Euro-International Symposium on Computational Intelligence (E-ISCI), Kosice, 2002.
I. Czarnowski & P. Jedrzejowicz. An Agent-Based PLA for the Cascade Correlation Learning Architecture. Springer-Verlag, Lecture Notes in Computer Science (to appear in 2005)
S.E. Fahlman & C. Lebiere. The Cascade-Corelation Learning Architecture. Advances in Neural Information Processing Systems 2, Morgan Kaufmann, 1990.
J. Hertz, A. Krogh, R.G. Palmer. Introduction to the Theory of Neural Computation. WNT, Warsaw, 1995 (in Polish).
P. Jedrzejowicz. Social Learning Algorithm as a Tool for Solving Some Difficult Scheduling Problems. Foundation of Computing and Decision Sciences, 1999, 24:51–66.
O.L. Mangasarian & W.H. Wolberg. Cancer Diagnosis via Linear Programming. SIAM News, 1990, 23(5):1–18.
C.J. Merz & M. Murphy. UCI Repository of machine learning databases. [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science, 1998.
Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin, 1996.
H.V.D. Parunak. Agents in Overalls: Experiences and Issues in the Development and Deployment of Industrial Agent-Based Systems. International Journal of Cooperative Information Systems, 2000, 9(3):209–228.
Yi Shang & B.W. Wah. A Global Optimization Method for Neural Network Training. Conference of Neural Networks. IEEE Computer, 1996, 29: 45–54.
B.W. Wah & Minglun Qian. Constrained Formulations for Neural Network Training and Their Applications to Solve the Two-Spiral Problem. Proceedings of the Fifth International Conference on Computer Science and Informatics, 2000, 1:598–601.
D. Whitley. Genetic Algorithms and Neural Networks. Genetic Algorithms in Enginering and Computer Science, G. Winter, J. Perianx, M. Galan and P. Cuesta (eds.), John Wiley 1995, 203–216.
S. Talukdar, L. Baerentzen, A. Gove, P. de Souza. Asynchronous Teams: Co-operation Schemes for Autonomous, Computer-Based Agents, Technical Report EDRC 18-59-96, Carnegie Mellon University, Pittsburgh, 1996.
Xiao-Feng Xie and Wen-Jun Zhang. SWAF: Swarm Algorithm Framework for Numerical Optimization. In: Kalyanmoy Deb et al (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2004), Heidelberg, Germany, June 2004. Seattle, WA, Springer Verlag, Lecture Notes in Computer Science, 2004, 3102:238–250.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag London Limited
About this paper
Cite this paper
Czarnowski, I., Jedrzejowicz, P. (2006). An Agent-Based Approach to ANN Training. In: Bramer, M., Coenen, F., Allen, T. (eds) Research and Development in Intelligent Systems XXII. SGAI 2005. Springer, London. https://doi.org/10.1007/978-1-84628-226-3_15
Download citation
DOI: https://doi.org/10.1007/978-1-84628-226-3_15
Publisher Name: Springer, London
Print ISBN: 978-1-84628-225-6
Online ISBN: 978-1-84628-226-3
eBook Packages: Computer ScienceComputer Science (R0)