Bayesian Tabu Search
Bayesian Tabu Search (BTS) is a hybrid metaheuristic that couples the memory-based forbidden-move mechanism of classic Tabu Search with a Bayesian probabilistic model. The Bayesian component learns from past evaluations to score candidate moves, focusing the search on promising regions while the tabu list prevents cycling. This combination reduces wasted function evaluations in expensive combinatorial and continuous optimization problems.
Kilderegistrering
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- Glover, F. (1989). Tabu search — Part I. ORSA Journal on Computing, 1(3), 190–206. · DOI 10.1287/ijoc.1.3.190
- Bergstra, J., Bardenet, R., Bengio, Y., Kegl, B. (2011). Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems (NIPS), 24, 2546–2554. · URL
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