no code implementations • 25 Aug 2022 • Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer
To this end, we develop a multi-objective Bayesian evolutionary optimization approach to HE-MOPs by exploiting the different data sets on the cheap and expensive objectives in HE-MOPs to alleviate the search bias caused by the heterogeneous evaluation costs for evaluating different objectives.
no code implementations • 7 Jun 2022 • Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer
Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency.
no code implementations • 30 Aug 2021 • Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer
Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time.
no code implementations • 7 Jun 2020 • Xilu Wang
The segmentation is obtained by components posterior probabilities and the rotations in pose variations are learned by the factor loading matrices.