Parameter tuning, that is, to find appropriate parameter settings (or configurations) of algorithms so that their performance is optimized, is an important task in the development and application of metaheuristics. Automating this task, i.e., developing algorithmic procedure to address parameter tuning task, is highly desired and has attracted significant attention from the researchers and practitioners. During last two decades, many automatic parameter tuning approaches have been proposed. This paper presents a comprehensive survey of automatic parameter tuning methods for metaheuristics. A new classification (or taxonomy) of automatic parameter tuning methods is introduced according to the structure of tuning methods. The existing automatic parameter tuning approaches are consequently classified into three categories: 1) simple generate-evaluate methods; 2) iterative generate-evaluate methods; and 3) high-level generate-evaluate methods. Then, these three categories of tuning methods are reviewed in sequence. In addition to the description of each tuning method, its main strengths and weaknesses are discussed, which is helpful for new researchers or practitioners to select appropriate tuning methods to use. Furthermore, some challenges and directions of this field are pointed out for further research.

中文翻译：

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元启发式自动参数调整方法综述

参数调优，即寻找合适的算法参数设置（或配置）以优化其性能，是元启发式算法开发和应用中的一项重要任务。自动化这项任务，即开发算法程序来解决参数调整任务，是非常需要的，并引起了研究人员和从业人员的极大关注。在过去的二十年中，已经提出了许多自动参数调整方法。本文对元启发式的自动参数调整方法进行了全面调查。根据调整方法的结构，引入了自动参数调整方法的新分类（或分类法）。因此，现有的自动参数调整方法分为三类：1) 简单的生成-评估方法；2) 迭代生成-评估方法；3) 高级生成-评估方法。然后，依次回顾这三类调优方法。除了对每种调优方法的描述外，还讨论了其主要优点和缺点，这有助于新的研究人员或从业人员选择合适的调优方法来使用。此外，还指出了该领域的一些挑战和方向以供进一步研究。这有助于新的研究人员或从业者选择合适的调优方法来使用。此外，指出了该领域的一些挑战和方向以供进一步研究。这有助于新的研究人员或从业者选择合适的调优方法来使用。此外，指出了该领域的一些挑战和方向以供进一步研究。