Max-value Entropy Search for Multi-Objective Bayesian Optimization
Max-value Entropy Search for Multi-Objective Bayesian Optimization
复制标题
DOI：
--
复制
发表时间：
2020-09
期刊：
影响因子：
--
通讯作者：
Syrine Belakaria;Aryan Deshwal;J. Doppa
中科院分区：
文献类型：
--
作者：
Syrine Belakaria;Aryan Deshwal;J. Doppa
我们考虑使用昂贵的功能评估的多目标（MO）BlackBox优化的问题，该问题的目标是通过最大程度地减少功能评估的数量来近似于解决方案的真实帕累托集合。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。我们提出了一种新的方法，称为最大值熵搜索多目标优化（MESMO）来解决此问题。 MESMO采用基于输出空间熵的采集函数来有效地选择输入序列，以快速发现高质量的解决方案。我们还提供了理论分析以表征中莫的功效。我们对几个合成和现实基准问题的实验表明，中莫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 by 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 approach referred to as Max-value Entropy Search for Multi-objective Optimization (MESMO) to solve this problem. MESMO employs an output-space entropy based acquisition function to efficiently select the sequence of inputs for evaluation for quickly uncovering high-quality solutions. We also provide theoretical analysis to characterize the efficacy of MESMO. Our experiments on several synthetic and real-world benchmark problems show that MESMO consistently outperforms state-of-the-art algorithms.