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.

翻译：代理优化（Surrogate Optimization, SO）算法在优化昂贵的黑箱函数方面展现出潜力。然而，其性能严重受限于与采样和代理拟合相关的超参数，这对其广泛应用构成了挑战。我们研究了超参数对各类SO算法的影响，并提出了一种面向SO的超参数自适应搜索方法（HASSO）。HASSO并非超参数调优算法，而是一种通用的自调整SO算法，它在优化主目标函数的同时动态调整自身超参数，且无需额外评估开销。该方法旨在提升SO算法对实践者的易用性、有效性和收敛速度。我们针对每个具体问题和SO方法识别并修正影响最显著的超参数，从而在不显著增加计算负担的前提下减少手动调优需求。实验结果表明，HASSO能在不同全局优化测试问题上有效提升多种SO算法的性能。