论文标题
批处理贝叶斯优化中采集功能的动态多目标合奏
Dynamic Multi-objective Ensemble of Acquisition Functions in Batch Bayesian Optimization
论文作者
论文摘要
贝叶斯优化（BO）是解决昂贵优化问题的典型方法。在BO的每次迭代中，使用先前评估的解决方案训练了高斯工艺（GP）模型。然后，推荐下一个用于昂贵评估的候选解决方案，通过在训练有素的替代模型上最大化廉价评估的采集功能。采集函数在优化过程中起着至关重要的作用。但是，每个采集函数都有其自己的优势和劣势，并且在各种问题上，没有一个单一的获取功能能够始终如一地超越其他函数。为了更好地利用不同采集功能的优势，我们为Batch BO提出了一种新方法。在每次迭代中，根据其当前和历史性能从集合中动态选择三个采集函数，以形成多目标优化问题（MOP）。使用进化多目标算法来优化这种拖把，可以获得一组非主导的解决方案。为了选择批处理解决方案，我们根据它们在三个采集函数上的相对性能将这些非主导的解决方案对几层进行排名。经验结果表明，所提出的方法与有关不同问题的最新方法具有竞争力。
Bayesian optimization (BO) is a typical approach to solve expensive optimization problems. In each iteration of BO, a Gaussian process(GP) model is trained using the previously evaluated solutions; then next candidate solutions for expensive evaluation are recommended by maximizing a cheaply-evaluated acquisition function on the trained surrogate model. The acquisition function plays a crucial role in the optimization process. However, each acquisition function has its own strengths and weaknesses, and no single acquisition function can consistently outperform the others on all kinds of problems. To better leverage the advantages of different acquisition functions, we propose a new method for batch BO. In each iteration, three acquisition functions are dynamically selected from a set based on their current and historical performance to form a multi-objective optimization problem (MOP). Using an evolutionary multi-objective algorithm to optimize such a MOP, a set of non-dominated solutions can be obtained. To select batch candidate solutions, we rank these non-dominated solutions into several layers according to their relative performance on the three acquisition functions. The empirical results show that the proposed method is competitive with the state-of-the-art methods on different problems.
