Global optimization for mixed categorical-continuous variables based on Gaussian process models with a randomized categorical space exploration step
type: Item
instance of: scholarly article
title: Global optimization for mixed categorical-continuous variables based on Gaussian process models with a randomized categorical space exploration step
DOI: 10.1080/03155986.2020.1730677
author: Delphine Sinoquet
author: M. Munoz Zuniga
published in: INFOR: Information Systems and Operational Research
publication date: 2023-03-15T00:00:00Z
Mathematics Subject Classification ID: 90C26
zbMATH DE Number: 7663657
zbMATH Keywords: derivative free optimization
zbMATH Keywords: surrogate models
zbMATH Keywords: EGO
zbMATH Keywords: NOMAD
zbMATH Keywords: categorical variables
describes a project that uses: MEMSS
describes a project that uses: EGO
describes a project that uses: DFL
describes a project that uses: SPACE
describes a project that uses: DiceKriging
describes a project that uses: DiceOptim
describes a project that uses: tgp
describes a project that uses: LBFGS-B
describes a project that uses: DFLBOX
describes a project that uses: MISO
describes a project that uses: S-PLUS
describes a project that uses: RBFOpt
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1080/03155986.2020.1730677
OpenAlex ID: W3011495853
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description: scientific article; zbMATH DE number 7663657