TY - GEN
T1 - Noisy multiobjective black-box optimization using Bayesian optimization
AU - Wang, Hongyan
AU - Xu, Hua
AU - Yuan, Yuan
AU - Deng, Junhui
AU - Sun, Xiaomin
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/7/13
Y1 - 2019/7/13
N2 - Expensive black-box problems are usually optimized by Bayesian Optimization (BO) since it can reduce evaluation costs via cheaper surrogates. The most popular model used in Bayesian Optimization is the Gaussian process (GP) whose posterior is based on a joint GP prior built by initial observations, so the posterior is also a Gaussian process. Observations are often not noise-free, so in most of these cases, a noisy transformation of the objective space is observed. Many single objective optimization algorithms have succeeded in extending efficient global optimization (EGO) to noisy circumstances, while ParEGO fails to consider noise. In order to deal with noisy expensive black-box problems, we extending ParEGO to noisy optimization according to adding a Gaussian noisy error while approximating the surrogate. We call it noisy-ParEGO and results of S-metric indicate that the algorithm works well on optimizing noisy expensive multiobjective black-box problems.
AB - Expensive black-box problems are usually optimized by Bayesian Optimization (BO) since it can reduce evaluation costs via cheaper surrogates. The most popular model used in Bayesian Optimization is the Gaussian process (GP) whose posterior is based on a joint GP prior built by initial observations, so the posterior is also a Gaussian process. Observations are often not noise-free, so in most of these cases, a noisy transformation of the objective space is observed. Many single objective optimization algorithms have succeeded in extending efficient global optimization (EGO) to noisy circumstances, while ParEGO fails to consider noise. In order to deal with noisy expensive black-box problems, we extending ParEGO to noisy optimization according to adding a Gaussian noisy error while approximating the surrogate. We call it noisy-ParEGO and results of S-metric indicate that the algorithm works well on optimizing noisy expensive multiobjective black-box problems.
KW - Black-box optimization
KW - Expensive multiobjective optimization
KW - Gaussian noise
KW - Gaussian Process
KW - ParEGO
UR - http://www.scopus.com/inward/record.url?scp=85070587431&partnerID=8YFLogxK
U2 - 10.1145/3319619.3321898
DO - 10.1145/3319619.3321898
M3 - 会议稿件
AN - SCOPUS:85070587431
T3 - GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
SP - 239
EP - 240
BT - GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2019 Genetic and Evolutionary Computation Conference, GECCO 2019
Y2 - 13 July 2019 through 17 July 2019
ER -