Name
Abstract | ||
|---|---|---|
Cost-aware optimization is a common and important problem in real-world optimizations. Since real-world optimization problems are costly and have no specific mathematical formula, Bayesian optimization (BO) is frequently used to optimize these black-box expensive functions. Typically, a total budget is assigned for BO to find the optimal solution, but how to efficiently use the given budget has not been carefully investigated. In this paper, we propose a single-objective cost-aware BO framework to efficiently optimize an expensive black-box function with regard to the budget. Our proposed method utilizes a multi-armed bandit algorithm to quickly figure out a suitable strategy to deal with the cost of the optimization problem. It is flexible in adapting to different types of optimum-cost relations, extendable to multiple strategies, and simple to implement. We conduct a comprehensive set of experiments on both synthetic and real-world optimization problems to demonstrate the advantages of our method. Experimental results show that our proposed method outperforms other cost-aware BO methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.knosys.2021.107481 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Bayesian optimization,Cost-aware,Multi-armed bandit,Expected improvement | Journal | 232 |
ISSN | Citations | PageRank |
0950-7051 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Phuc Luong | 1 | 0 | 0.34 |
| Dang Nguyen | 2 | 0 | 4.73 |
| Sunil Gupta | 3 | 3 | 6.15 |
| Santu Rana | 4 | 113 | 34.26 |
| Svetha Venkatesh | 5 | 4190 | 425.27 |