Bayesian Optimization for Categorical and Mixed Variables Using a Multinomial Logit Surrogate
Bayesian optimization (BO) is a widely used framework for optimizing expensive black-box functions. Most BO methods rely on Gaussian process (GP) surrogates, which perform well in continuous domains but encounter difficulties when decision variables include categorical or mixed discrete–continuous c...
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| Hoofdauteurs: | , |
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| Formaat: | Artigo |
| Taal: | Inglês |
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MDPI AG
2026-05-01
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| Reeks: | Algorithms |
| Onderwerpen: | |
| Online toegang: | https://www.mdpi.com/1999-4893/19/5/361 |
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