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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Principais autores:	Muhammad Amir Saeed, Antonio Candelieri
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	MDPI AG 2026-05-01
סדרה:	Algorithms
נושאים:	Bayesian optimization categorical variables multinomial logit model black-box optimization discrete optimization
גישה מקוונת:	https://www.mdpi.com/1999-4893/19/5/361
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