Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.
Version: 1.1.8 Depends: DiceKriging, emoa Imports: Rcpp (≥ 0.12.15), methods, rgenoud, pbivnorm, pso, randtoolbox, KrigInv, MASS, DiceDesign, ks, rgl LinkingTo: Rcpp Suggests: knitr, DiceOptim Published: 2024-01-26 DOI: 10.32614/CRAN.package.GPareto Author: Mickael Binois, Victor Picheny Maintainer: Mickael Binois <mickael.binois at inria.fr> BugReports: https://github.com/mbinois/GPareto/issues License: GPL-3 URL: https://github.com/mbinois/GPareto NeedsCompilation: yes Citation: GPareto citation info Materials: README NEWS In views: Optimization CRAN checks: GPareto results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=GPareto to link to this page.
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