GPareto (1.1.3)

0 users

Gaussian Processes for Pareto Front Estimation and Optimization.

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.

Maintainer: Mickael Binois
Author(s): Mickael Binois, Victor Picheny

License: GPL-3

Uses: DiceDesign, DiceKriging, emoa, KrigInv, ks, MASS, pbivnorm, pso, randtoolbox, Rcpp, rgenoud, rgl, knitr
Reverse suggests: DiceOptim

Released about 1 month ago.

7 previous versions



  (0 votes)


  (0 votes)

Log in to vote.


No one has written a review of GPareto yet. Want to be the first? Write one now.

Related packages:(20 best matches, based on common tags.)

Search for GPareto on google, google scholar, r-help, r-devel.

Visit GPareto on R Graphical Manual.