Surrogate Optimization (SO) algorithms have shown promise for optimizing expensive black-box functions. However, their performance is heavily influenced by hyperparameters related to sampling and surrogate fitting, which poses a challenge to their widespread adoption. We investigate the impact of hyperparameters on various SO algorithms and propose a Hyperparameter Adaptive Search for SO (HASSO) approach. HASSO is not a hyperparameter tuning algorithm, but a generic self-adjusting SO algorithm that dynamically tunes its own hyperparameters while concurrently optimizing the primary objective function, without requiring additional evaluations. The aim is to improve the accessibility, effectiveness, and convergence speed of SO algorithms for practitioners. Our approach identifies and modifies the most influential hyperparameters specific to each problem and SO approach, reducing the need for manual tuning without significantly increasing the computational burden. Experimental results demonstrate the effectiveness of HASSO in enhancing the performance of various SO algorithms across different global optimization test problems.

对于优化昂贵的黑盒函数，代理优化算法表现出了一定的潜力。然而，与采样和代理适配相关的超参数对其性能产生了很大影响，这给普及它们带来了挑战。我们研究了超参数对各种代理优化算法的影响，并提出了一种名为Hyperparameter Adaptive Search for SO（HASSO）的方法。HASSO不是一个超参数调整算法，而是一种通用的自适应代理优化算法，通过动态调整自己的超参数同时优化主要目标函数，而无需额外的评估。目标是提高代理优化算法对实践者的可访问性、效果和收敛速度。我们的方法确定并修改了每个问题和代理方法相关的最有影响力的超参数，减少了手动调整的需要，同时不显著增加计算负担。实验结果证明了HASSO在加强各种代理优化算法在不同全局优化测试问题上的性能方面的有效性。

超参数自适应搜索用于代理优化的一种自调整方法