GLIS - Global and preference-based optimization of expensive black-box functions using inverse distance weighting and radial basis function surrogates.
Keywords for this software
References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Bacigalupo, Andrea; Gnecco, Giorgio; Lepidi, Marco; Gambarotta, Luigi: Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization (2021)
- Bemporad, Alberto; Piga, Dario: Global optimization based on active preference learning with radial basis functions (2021)
- Benavoli, Alessio; Azzimonti, Dario; Piga, Dario: A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with skew Gaussian processes (2021)
- Masti, Daniele; Bemporad, Alberto: Learning nonlinear state-space models using autoencoders (2021)
- Sabug, Lorenzo; Ruiz, Fredy; Fagiano, Lorenzo: SMGO: a set membership approach to data-driven global optimization (2021)
- Bemporad, Alberto: Global optimization via inverse distance weighting and radial basis functions (2020)