GPareto swMATH ID: 28711 Software Authors: Mickaël Binois; Victor Picheny Description: R package GPareto: 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. Homepage: https://cran.r-project.org/web/packages/GPareto/index.html Source Code: https://github.com/cran/GPareto Dependencies: R Keywords: kriging; Pareto front; efficient global optimization; uncertainty quantification; Journal of Statistical Software; R; R package Related Software: ParEGO; EGO; MOEA/D; R; HypE; PESC; DiceOptim; DiceEval; KrigInv; DiceDesign; rgenoud; DiceKriging; Hyperopt; nsga2R; Matlab; goalprog; mopsocd; mco; STK; NLopt Cited in: 4 Documents Standard Articles 1 Publication describing the Software Year all top 5 Cited by 12 Authors 1 Aubert, Stéphane 1 Bect, Julien 1 Binois, Mickaël 1 Ducros, Frédéric 1 Feliot, Paul 1 Habbal, Abderrahmane 1 Mastrippolito, Franck 1 Picheny, Victor 1 Taillandier, Patrick 1 Vazquez, Emmanuel 1 Xing, Huanlai 1 Zhan, Dawei Cited in 3 Serials 2 Journal of Global Optimization 1 Computers and Fluids 1 Journal of Machine Learning Research (JMLR) Cited in 3 Fields 2 Operations research, mathematical programming (90-XX) 1 Computer science (68-XX) 1 Fluid mechanics (76-XX) Citations by Year