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A tabu search algorithm for the training of neural networks

Author

Listed:
  • B Dengiz

    (Baskent University)

  • C Alabas-Uslu

    (Maltepe University)

  • O Dengiz

    (Auburn University)

Abstract

The most widely used training algorithm of neural networks (NNs) is back propagation (BP), a gradient-based technique that requires significant computational effort. Metaheuristic search techniques such as genetic algorithms, tabu search (TS) and simulated annealing have been recently used to cope with major shortcomings of BP such as the tendency to converge to a local optimal and a slow convergence rate. In this paper, an efficient TS algorithm employing different strategies to provide a balance between intensification and diversification is proposed for the training of NNs. The proposed algorithm is compared with other metaheuristic techniques found in literature using published test problems, and found to outperform them in the majority of the test cases.

Suggested Citation

  • B Dengiz & C Alabas-Uslu & O Dengiz, 2009. "A tabu search algorithm for the training of neural networks," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(2), pages 282-291, February.
    • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:2:d:10.1057_palgrave.jors.2602535
    DOI: 10.1057/palgrave.jors.2602535
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    References listed on IDEAS

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    6. Sexton, Randall S. & Alidaee, Bahram & Dorsey, Robert E. & Johnson, John D., 1998. "Global optimization for artificial neural networks: A tabu search application," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 570-584, April.
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