Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design

Wenlong Lyu, Fan Yang, Changhao Yan, Dian Zhou, Xuan Zeng
; Proceedings of the 35th International Conference on Machine Learning, PMLR 80:3306-3314, 2018.

Abstract

Bayesian optimization methods are promising for the optimization of black-box functions that are expensive to evaluate. In this paper, a novel batch Bayesian optimization approach is proposed. The parallelization is realized via a multi-objective ensemble of multiple acquisition functions. In each iteration, the multi-objective optimization of the multiple acquisition functions is performed to search for the Pareto front of the acquisition functions. The batch of inputs are then selected from the Pareto front. The Pareto front represents the best trade-off between the multiple acquisition functions. Such a policy for batch Bayesian optimization can significantly improve the efficiency of optimization. The proposed method is compared with several state-of-the-art batch Bayesian optimization algorithms using analytical benchmark functions and real-world analog integrated circuits. The experimental results show that the proposed method is competitive compared with the state-of-the-art algorithms.

Cite this Paper


BibTeX
@InProceedings{pmlr-v80-lyu18a, title = {Batch {B}ayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design}, author = {Lyu, Wenlong and Yang, Fan and Yan, Changhao and Zhou, Dian and Zeng, Xuan}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {3306--3314}, year = {2018}, editor = {Jennifer Dy and Andreas Krause}, volume = {80}, series = {Proceedings of Machine Learning Research}, address = {Stockholmsmässan, Stockholm Sweden}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/lyu18a/lyu18a.pdf}, url = {http://proceedings.mlr.press/v80/lyu18a.html}, abstract = {Bayesian optimization methods are promising for the optimization of black-box functions that are expensive to evaluate. In this paper, a novel batch Bayesian optimization approach is proposed. The parallelization is realized via a multi-objective ensemble of multiple acquisition functions. In each iteration, the multi-objective optimization of the multiple acquisition functions is performed to search for the Pareto front of the acquisition functions. The batch of inputs are then selected from the Pareto front. The Pareto front represents the best trade-off between the multiple acquisition functions. Such a policy for batch Bayesian optimization can significantly improve the efficiency of optimization. The proposed method is compared with several state-of-the-art batch Bayesian optimization algorithms using analytical benchmark functions and real-world analog integrated circuits. The experimental results show that the proposed method is competitive compared with the state-of-the-art algorithms.} }
Endnote
%0 Conference Paper %T Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design %A Wenlong Lyu %A Fan Yang %A Changhao Yan %A Dian Zhou %A Xuan Zeng %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-lyu18a %I PMLR %J Proceedings of Machine Learning Research %P 3306--3314 %U http://proceedings.mlr.press %V 80 %W PMLR %X Bayesian optimization methods are promising for the optimization of black-box functions that are expensive to evaluate. In this paper, a novel batch Bayesian optimization approach is proposed. The parallelization is realized via a multi-objective ensemble of multiple acquisition functions. In each iteration, the multi-objective optimization of the multiple acquisition functions is performed to search for the Pareto front of the acquisition functions. The batch of inputs are then selected from the Pareto front. The Pareto front represents the best trade-off between the multiple acquisition functions. Such a policy for batch Bayesian optimization can significantly improve the efficiency of optimization. The proposed method is compared with several state-of-the-art batch Bayesian optimization algorithms using analytical benchmark functions and real-world analog integrated circuits. The experimental results show that the proposed method is competitive compared with the state-of-the-art algorithms.
APA
Lyu, W., Yang, F., Yan, C., Zhou, D. & Zeng, X.. (2018). Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design. Proceedings of the 35th International Conference on Machine Learning, in PMLR 80:3306-3314

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