\(\mathcal G\)-SELC: optimization by sequential elimination of level combinations using genetic algorithms and Gaussian processes

DOI10.1214/08-AOAS199zbMath1165.62093arXiv0906.1433OpenAlexW3099065896MaRDI QIDQ1018629

C. F. Jeff Wu, Pritam Ranjan, Abhyuday Mandal

Publication date: 20 May 2009

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0906.1433

zbMATH Keywords

krigingprocess optimizationbatch-sequential designexpected improvement function

Mathematics Subject Classification ID

Design of statistical experiments (62K99) Applications of statistics in engineering and industry; control charts (62P30) Sequential statistical methods (62L99)

Related Items

Solving an inverse problem for time-series-valued computer simulators via multiple contour estimation ⋮ Layers of Experiments with Adaptive Combined Design

Uses Software

• EGO

Cites Work

• Model-independent prediction methods in pharmacokinetics: Theoretical considerations
• Efficient global optimization of expensive black-box functions
• Interpolation of spatial data. Some theory for kriging
• Minimax distance designs in two-level factorial experiments
• The design and analysis of computer experiments.
• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• The Tunneling Algorithm for the Global Minimization of Functions
• Orthogonal Array-Based Latin Hypercubes
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item