Gaussian Processes for Pareto Front Estimation and Optimization
Gaussian process regression models, a.k.a. kriging models, are
applied to global multiobjective optimization of black-box functions.
Multiobjective Expected Improvement and Stepwise Uncertainty Reduction
sequential infill criteria are available. A quantification of uncertainty
on Pareto fronts is provided using conditional simulations.
Rcpp (>= 0.11.1), rgenoud, pbivnorm, pso, randtoolbox,
KrigInv, MASS

stats, grDevices, graphics, Rcpp (>= 0.11.1), rgenoud,
pbivnorm, pso, randtoolbox, KrigInv, MASS

Gaussian process regression models, a.k.a. kriging models, are
applied to global multiobjective optimization of black-box functions.
Multiobjective Expected Improvement and Step-wise Uncertainty Reduction
sequential infill criteria are available. A quantification of uncertainty
on Pareto fronts is provided using conditional simulations.
Rcpp (>= 0.11.1), rgenoud, pbivnorm, pso, randtoolbox,
KrigInv, MASS, DiceDesign (>= 1.4), ks

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.

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.

Rcpp (>= 0.12.15), methods, rgenoud, pbivnorm, pso,
randtoolbox, KrigInv, MASS, DiceDesign, ks, rgl

Blake MacDoanld, Hugh Chipman, Pritam Ranjan
A computationally stable approach of fitting a Gaussian
process (GP) model to a deterministic simulator.

A computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator.