Gpareto: An r package for gaussian-process-based multi-objective optimization and analysis

0Citations
Citations of this article
39Readers
Mendeley users who have this article in their library.

Abstract

The GPareto package for R provides multi-objective optimization algorithms for expensive black-box functions and an ensemble of dedicated uncertainty quantification methods. Popular methods such as efficient global optimization in the mono-objective case rely on Gaussian processes or kriging to build surrogate models. Driven by the prediction uncertainty given by these models, several infill criteria have also been proposed in a multi-objective setup to select new points sequentially and efficiently cope with severely limited evaluation budgets. They are implemented in the package, in addition with Pareto front estimation and uncertainty quantification visualization in the design and objective spaces. Finally, it attempts to fill the gap between expert use of the corresponding methods and user-friendliness, where many efforts have been put on providing graphical post-processing, standard tuning and interactivity.

Cite

CITATION STYLE

APA

Binois, M., & Picheny, V. (2019). Gpareto: An r package for gaussian-process-based multi-objective optimization and analysis. Journal of Statistical Software, 89(1). https://doi.org/10.18637/jss.v089.i08

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free