On Sampling Methods for Costly Multi-Objective Black-Box Optimization

DOI10.1007/978-3-319-29975-4_15zbMath1355.90095DBLPbooks/sp/16/SteponaviceSHSV16OpenAlexW2548533198WikidataQ62033310 ScholiaQ62033310MaRDI QIDQ2958628

Ingrida Steponavičė, Mojdeh Shirazi-Manesh, Rob Hyndman, Laura Villanova, Kate A. Smith-Miles

Publication date: 3 February 2017

Published in: Advances in Stochastic and Deterministic Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-29975-4_15

zbMATH Keywords

design of experimentspace-fillingefficient global optimizationlow-discrepancy

Mathematics Subject Classification ID

Sampling theory, sample surveys (62D05) Nonconvex programming, global optimization (90C26) Multi-objective and goal programming (90C29)

Related Items (2)

An upper bound on the Hausdorff distance between a Pareto set and its discretization in bi-objective convex quadratic optimization ⋮ Bayesian optimization of pump operations in water distribution systems

Uses Software

• ParEGO
• mlr
• mlrMBO
• GitHub
• weightedHypervolume

Cites Work

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