The field of algorithmic optimization has significantly advanced with the
development of methods for the automatic configuration of algorithmic
parameters. This article delves into the Algorithm Configuration Problem,
focused on optimizing parametrized algorithms for solving specific instances of
decision/optimization problems. We present a comprehensive framework that not
only formalizes the Algorithm Configuration Problem, but also outlines
different approaches for its resolution, leveraging machine learning models and
heuristic strategies. The article categorizes existing methodologies into
per-instance and per-problem approaches, distinguishing between offline and
online strategies for model construction and deployment. By synthesizing these
approaches, we aim to provide a clear pathway for both understanding and
addressing the complexities inherent in algorithm configuration.

算法优化领域已经通过自动配置算法参数的方法显著进展。本文深入研究了算法配置问题，重点优化针对特定决策 / 优化问题实例的参数化算法。我们提出了一个全面的框架，不仅形式化了算法配置问题，还概述了利用机器学习模型和启发式策略解决该问题的不同方法。本文将已有的方法论分为按实例和按问题的方法，并区分了模型构建和部署的离线和在线策略。通过综合这些方法，我们旨在为理解和应对算法配置中固有的复杂性提供清晰的路径。