Amadou Ba, Fearghal O'Donncha, et al.
INFORMS 2023
We propose the StepDIRECT algorithm for derivative-free optimization (DFO), in which the black-box objective function has a stepwise landscape. Our framework is based on the well-known DIRECT algorithm. By incorporating the local variability to explore the flatness, we provide a new criterion to select the potentially optimal hyper-rectangles. In addition, we introduce a stochastic local search algorithm performing on potentially optimal hyper-rectangles to improve the solution quality and convergence speed. Global convergence of the StepDIRECT algorithm is provided. Numerical experiments on optimization for random forest models and hyper-parameter tuning are presented to support the efficacy of our algorithm.
Amadou Ba, Fearghal O'Donncha, et al.
INFORMS 2023
Dhaval Patel, Dzung Phan, et al.
ICDE 2022
Robert Baseman
TechConnect 2024
Ademir Ferreira Da Silva, Levente Klein, et al.
INFORMS 2022