Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Bayesian Tabu Search× | Bayesi optimeerimine – järjestikune mudelipõhine hüperparameetrite häälestamine× | |
|---|---|---|
| Valdkond≠ | Simulatsioon | Optimeerimine |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 1989 (tabu search); hybrid formulations ~2005–2015 | 1975 (foundational); 2012 (ML standard) |
| Looja≠ | Glover, F. (tabu search); Bayesian integration developed by multiple researchers in the 2000s–2010s | Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012) |
| Tüüp≠ | Hybrid metaheuristic — memory-based local search with Bayesian probabilistic guidance | Sequential model-based black-box optimization |
| Algallikas≠ | Glover, F. (1989). Tabu search — Part I. ORSA Journal on Computing, 1(3), 190–206. DOI ↗ | Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25. link ↗ |
| Rööpnimetused | BTS, Bayesian-guided tabu search, probabilistic tabu search, Bayes-TS | Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO |
| Seotud≠ | 6 | 2 |
| Kokkuvõte≠ | 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. | Bayesian Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate model — typically a Gaussian Process — to past observations and uses an acquisition function to decide where to probe next, balancing exploration of unknown regions with exploitation of promising ones. |
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