GPareto: Gaussian Processes for Pareto Front Estimation and Optimization (original) (raw)
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 |
| In views: | Optimization |
| CRAN checks: | GPareto results |
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