论文标题
多目标贝叶斯优化的不确定性感知搜索框架
Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization
论文作者
论文摘要
我们考虑使用昂贵的功能评估的多目标（MO）BlackBox优化的问题，该问题的目标是近似真正的帕累托解决方案集，同时最小化功能评估的数量。例如，在硬件设计优化中，我们需要使用昂贵的模拟来找到权衡性能，能源和领域开销的设计。我们提出了一个新颖的不确定性感知搜索框架，称为USEMO，以有效地选择输入序列以评估以解决此问题。 USEMO的选择方法包括通过真实功能的替代模型来解决廉价的MO优化问题，以识别最有前途的候选人，并根据不确定性的度量选择最佳候选人。我们还提供理论分析以表征我们方法的功效。我们对几种综合和六个不同的现实基准问题的实验表明，USEMO始终优于最先进的算法。
We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions while minimizing the number of function evaluations. For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive simulations. We propose a novel uncertainty-aware search framework referred to as USeMO to efficiently select the sequence of inputs for evaluation to solve this problem. The selection method of USeMO consists of solving a cheap MO optimization problem via surrogate models of the true functions to identify the most promising candidates and picking the best candidate based on a measure of uncertainty. We also provide theoretical analysis to characterize the efficacy of our approach. Our experiments on several synthetic and six diverse real-world benchmark problems show that USeMO consistently outperforms the state-of-the-art algorithms.
