Abstract
Tabu Search is a metaheuristic that guides a local heuristic search procedure to explore the solution space beyond local optimality. One of the main components of tabu search is its use of adaptive memory, which creates a more flexible search behavior. Memory based strategies are therefore the hallmark of tabu search approaches, founded on a quest for “integrating principles,” by which alternative forms of memory are appropriately combined with effective strategies for exploiting them. In this chapter we address the problem of training multilayer feed-forward neural networks. These networks have been widely used for both prediction and classification in many different areas. Although the most popular method for training these networks is backpropagation, other optimization methods such as tabu search have been applied to solve this problem. This chapter describes two training algorithms based on the tabu search. The experimentation shows that the procedures provide high quality solutions to the training problem, and in addition consume a reasonable computational effort.
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
Bishop, C. M., 1995, Neural Networks for Pattern Recognition, Oxford University Press.
El-Fallahi A., 2002, Entrenamiento de Redes Neuronales, Trabajo de Investigatión, Dpto Estadśtica e I.O. University of Valencia.
El-Fallahi, A., Martí, R., and Lasdon, L., 2005, Path relinking and GRG for artificial neural networks, European journal of operational research 169:508–519.
Fahlman, S. E., 1988, An empirical study of learning speed in back-propagation networks, in: Connectionist Models Summer School, T. J. Sejnowski, G. E. Hinton and D. S. Touretzky, eds., San Mateo, CA, Morgan Kaufmann, pp. 38–51.
Glover, F., 1986, Future paths for integer programming and links to artificial intelligence, Computers and Operations Research 13:533–549.
Glover, F., 1989, Tabu search part I, ORSA Journal on Computing 1:190–206.
Glover, F., and Laguna, M., 1993, Tabu search, in: Modern Heuristic Techniques for Combinatorial Problems, C. Reeves, ed., Blackwell Scientific Publishing, Oxford, pp. 70–150.
Glover, F., and Laguna, M., 1997, Tabu Search, Kluwer Academic Publishers, Boston.
Jacobs, R. A., 1988, Increased rates of convergence through learning rate adaptation, Neural Networks 1:295–307.
Laguna, M., Martí, R., 2003, Scatter Search—Methodology and Implementations in C, Kluwer Academic Publishers, Boston.
Riedmiller, M., and Heinrich, B., 1993, A direct adaptive method for faster back-propagation learning: the RPROP algorithm, IEEE Intl. Conf. on Neural Networks, pp. 586–591.
Rumelhart, D. E., and McClelland, J. L., 1986, Parallel distributed processing: explorations in the microstructure of cognition, Cambridge, MA: The MIT Press.
Sexton, R. S., Alidaee, B., Dorsey, R. E., and Johnson, J. D., 1998, Global optimization for artificial neural networks: a tabu search application, European Journal of Operational Research 106:570–584.
Smith, S., and Lasdon, L., 1992, Solving large nonlinear programs using GRG, ORSA Journal on Computing 4(1): 2–15.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Glover, F., Marti, R. (2006). Tabu Search. In: Alba, E., Martí, R. (eds) Metaheuristic Procedures for Training Neutral Networks. Operations Research/Computer Science Interfaces Series, vol 36. Springer, Boston, MA . https://doi.org/10.1007/0-387-33416-5_3
Download citation
DOI: https://doi.org/10.1007/0-387-33416-5_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-33415-8
Online ISBN: 978-0-387-33416-5
eBook Packages: Business and EconomicsBusiness and Management (R0)