Abstract
In this paper we present the use of a genetic algorithm (GA) for the optimization, in clustering tasks, of a new kind of fast-learning neural network. The network uses a combination of supervised and un-supervised learning that makes it suitable for automatic tuning -by means of the GA-of the learning parameters and initial weights in order to obtain the highest recognition score. Simulation results are presented showing as, for relatively simple clustering tasks, the GA finds in a few generations the parameters of the network that lead to a classification accuracy close to 100%.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Ackley, D. and Littman, M: Interactions Between Learning and Evolution, in Artificial Life II Sante Fe Institute in the Sciences of Complexity, vol. X, pp 487–509. Edited by C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen. Redwood City, CA: Addison Wesley, 1991.
Belew, R.K., McInemey, J., and Schraudolf, N.N.: Evolving Networks: Using the Genetic Algorithm with Connectionist Learning, in Artificial Life II, Sante Fe Institute in the Sciences of Complexity, vol. X. pp 511–547. Edited by C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen. Redwood City, CA: Addison Wesley, 1991.
Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning, Reading, MA: Addison Wesley, 1989.
Kohonen, T.: The Self-Organizing Map, Proceedings of the IEEE, Vol 78, 9, 1464–1480, 1990.
Makhoul, J., Roucos, S., and Gish, H.: Vector Quantization in Speech Coding, Proceedings of the IEEE 73 (1985), 1551–1587.
Merelo, J.J., Patón, M., Cosano, J., Cillero, M., Bermúdez, A., Molina, J.A., and Morán, F.: Combination of Hebbian Neural Networks and Genetic Algorithms in Artificial Life Organisms, communication in Artificial Life III Conference, Santa Fe, NM, 1992.
Paredis, J.: The Evolution of Behaviour: Some Experiments, Procs. of the 1st Int. Conf. on Simulation of Adaptive Behaviour, Wilson, Meyer, Eds. 1991, pp 419–426.
Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, Cambridge, MA: The MIT Press, 1986.
Whitley, D.; Dominic, S.; Das, R.; Genetic Reinforcement Learning with Multilayer Neural Networks, Procs, ICGA 91, pp 562–569.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1993 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Merelo, J.J., Patón, M., Cañas, A., Prieto, A., Morán, F. (1993). Optimization of a competitive learning neural network by genetic algorithms. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_145
Download citation
DOI: https://doi.org/10.1007/3-540-56798-4_145
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-56798-1
Online ISBN: 978-3-540-47741-9
eBook Packages: Springer Book Archive