We investigate modifications to Bayesian Optimization for a resource-constrained setting of sequential experimental design where changes to certain design variables of the search space incur a switching cost. This models the scenario where there is a trade-off between evaluating more while maintaining the same setup, or switching and restricting the number of possible evaluations due to the incurred cost. We adapt two process-constrained batch algorithms to this sequential problem formulation, and propose two new methods: one cost-aware and one cost-ignorant. We validate and compare the algorithms using a set of 7 scalable test functions in different dimensionalities and switching-cost settings for 30 total configurations. Our proposed cost-aware hyperparameter-free algorithm yields comparable results to tuned process-constrained algorithms in all settings we considered, suggesting some degree of robustness to varying landscape features and cost trade-offs. This method starts to outperform the other algorithms with increasing switching-cost. Our work broadens out from other recent Bayesian Optimization studies in resource-constrained settings that consider a batch setting only. While the contributions of this work are relevant to the general class of resource-constrained problems, they are particularly relevant to problems where adaptability to varying resource availability is of high importance

翻译：我们研究了在资源受限的序列实验设计设置中对贝叶斯优化的改进，其中对搜索空间中某些设计变量的更改会产生切换成本。这模拟了一个权衡场景：在保持相同设置的情况下进行更多评估，或者因切换而限制可能的评估次数（由于产生的成本）。我们将两种过程约束的批处理算法适配到这一序列问题公式中，并提出了两种新方法：一种成本感知方法和一种成本无知方法。我们使用一组7个可扩展测试函数，在不同维度和切换成本设置下（共30种配置）验证并比较了这些算法。我们提出的无超参数成本感知算法在我们考虑的所有设置中均取得与调优后的过程约束算法相当的结果，表明其对不同景观特征和成本权衡具有一定程度的鲁棒性。随着切换成本的增加，该方法开始优于其他算法。我们的研究超越了近期仅考虑批处理设置的资源受限环境下的贝叶斯优化研究。虽然本工作的贡献与一般资源受限问题类别相关，但它特别适用于资源可用性适应性具有高度重要性的问题。