论文标题
在贝叶斯在计算昂贵的约束下寻找可行空间
On Bayesian Search for the Feasible Space Under Computationally Expensive Constraints
论文作者
论文摘要
我们通常有兴趣在多个约束下确定决策空间的可行子集，以允许有效的设计探索。如果确定可行性需要计算昂贵的模拟，则勘探成本将是过于刺激的。贝叶斯搜索对于此类问题的数据有效：从一个小数据集开始，中心概念是使用具有收购功能的约束模型来定位有希望的解决方案，这些解决方案可能会在增强数据集时提高可行性的预测。在这种有限数量的昂贵评估的顺序主动学习方法的结尾处，模型可以准确预测任何解决方案的可行性，从而消除了完全模拟的需求。在本文中，我们提出了一种新颖的采集函数，该功能结合了解决方案位于可行空间和不可行空间（表示剥削）和预测（代表勘探）中的熵之间的边界的可能性。实验证实了所提出功能的功效。
We are often interested in identifying the feasible subset of a decision space under multiple constraints to permit effective design exploration. If determining feasibility required computationally expensive simulations, the cost of exploration would be prohibitive. Bayesian search is data-efficient for such problems: starting from a small dataset, the central concept is to use Bayesian models of constraints with an acquisition function to locate promising solutions that may improve predictions of feasibility when the dataset is augmented. At the end of this sequential active learning approach with a limited number of expensive evaluations, the models can accurately predict the feasibility of any solution obviating the need for full simulations. In this paper, we propose a novel acquisition function that combines the probability that a solution lies at the boundary between feasible and infeasible spaces (representing exploitation) and the entropy in predictions (representing exploration). Experiments confirmed the efficacy of the proposed function.
