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: |
1.1.0 |
Depends: |
DiceKriging (≥ 1.5.3), emoa, methods |
Imports: |
Rcpp (≥ 0.11.1), rgenoud, pbivnorm, pso, randtoolbox, KrigInv, MASS, DiceDesign (≥ 1.4), ks |
LinkingTo: |
Rcpp |
Suggests: |
knitr |
Published: |
2017-06-29 |
Author: |
Mickael Binois, Victor Picheny |
Maintainer: |
Mickael Binois <mickael.binois at chicagobooth.edu> |
License: |
GPL-3 |
NeedsCompilation: |
yes |
Materials: |
NEWS |
CRAN checks: |
GPareto results |