Training Recurrent Neural Networks Using Tabu Search Algorithm

Date

1996-06-04

Author

Karaboğa, Derviş
Kalınlı, Adem

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There are several modern heuristic optimisation techniques, such as neural networks, genetic algorithms, simulated annealing and tabu search algorithms. Of these algorithms, the tabu search is quite a new, promising search technique for numeric problems, especially for nonlinear problems. However, the convergence speed of the standard tabu search to the global optimum is initial-solution-dependent, since it is a form of iterative search. In this paper, a new model of tabu searching, which has been proposed by the authors to overcome the drawback of a standard tabu search, is tested for training a recurrent neural network to identify dynamic systems.

URI

https://hdl.handle.net/11511/73228

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Unverified, Conference / Seminar

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Citation Formats

D. Karaboğa and A. Kalınlı, “Training Recurrent Neural Networks Using Tabu Search Algorithm,” 1996, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/73228.