通过动态丢弃变量提升高维贝叶斯优化效率
An Adaptive Dropout Approach for High-Dimensional Bayesian Optimization
- 迭代中自适应地丢弃采集函数的变量，逐步降维
- 在高维问题上显著提升解的质量，优于传统方法
- 适合求解昂贵的高维黑箱优化问题
贝叶斯优化（BO）广泛用于求解昂贵的黑箱优化问题。然而，其在高维问题上的表现因采集函数的高维性而显著下降。本文提出一种自适应丢弃（AdaDropout）方法，在迭代过程中动态丢弃采集函数的变量，逐步降低其维度，从而缓解优化难度。数值实验表明，AdaDropout能有效应对高维挑战，显著提升解的质量，优于标准贝叶斯优化方法，并在与当前先进高维贝叶斯优化方法的对比中表现更优。该工作为高维昂贵优化提供了一种简单而高效的解决方案。
原文摘要 · Abstract (English)
Bayesian optimization (BO) is a widely used algorithm for solving expensive black-box optimization problems. However, its performance decreases significantly on high-dimensional problems due to the inherent high-dimensionality of the acquisition function. In the proposed algorithm, we adaptively dropout the variables of the acquisition function along the iterations. By gradually reducing the dimension of the acquisition function, the proposed approach has less and less difficulty to optimize the acquisition function. Numerical experiments demonstrate that AdaDropout effectively tackle high-dimensional challenges and improve solution quality where standard Bayesian optimization methods often struggle. Moreover, it achieves superior results when compared with state-of-the-art high-dimensional Bayesian optimization approaches. This work provides a simple yet efficient solution for high-dimensional expensive optimization.
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