Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm

DOI10.1007/s10898-018-0609-2zbMath1406.90095OpenAlexW2788545772WikidataQ62736845 ScholiaQ62736845MaRDI QIDQ721156

Eric Bradford, Artur M. Schweidtmann, Alexei Lapkin

Publication date: 18 July 2018

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11250/2585347

zbMATH Keywords

hypervolumeglobal optimizationKrigingBayesian optimizationresponse surfacesexpensive-to-evaluate functions

Mathematics Subject Classification ID

Gaussian processes (60G15) Nonconvex programming, global optimization (90C26) Multi-objective and goal programming (90C29) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (7)

Computationally efficient integrated design and predictive control of flexible energy systems using multi‐fidelity simulation‐based Bayesian optimization ⋮ Stochastic tracking control of multivariable nonlinear systems subject to external disturbances ⋮ Hybrid Gaussian process modeling applied to economic stochastic model predictive control of batch processes ⋮ Correction to: ``Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm ⋮ Performance indicators in multiobjective optimization ⋮ MVMOO: mixed variable multi-objective optimisation ⋮ Deterministic global optimization with Gaussian processes embedded

Uses Software

• Matlab
• DIRECT
• EGO
• ParEGO
• SUMO
• GPML
• GitHub
• NGPM
• Pareto Set
• Hypervolume Indicator
• ParEGO_Eigen
• TS-EMO

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• Computing 3-D Expected Hypervolume Improvement and Related Integrals in Asymptotically Optimal Time
• Advanced Lectures on Machine Learning

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