CRAN

GPareto 1.1.3

Gaussian Processes for Pareto Front Estimation and Optimization

Released May 9, 2019 by Mickael Binois

Dependencies

ks 1.11.4 randtoolbox 1.30.0 KrigInv 1.3.1 rgl 0.100.19 DiceKriging 1.5.6 Rcpp emoa 0.5-0 rgenoud 5.8-3.0 MASS 7.3-51.4 DiceDesign 1.8 pbivnorm 0.6.0 pso 1.0.3

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.

Source

View GitHub Mirror

Release History

• 1.1.3
• 1.1.2
• 1.1.1
• 1.1.0
• 1.0.3
• 1.0.2
• 1.0.1
• 1.0.0