During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this article, we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies that might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem and low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic that need further in-depth investigations.

中文翻译：

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机器学习到元启发式

在过去的几年里，应用机器学习 (ML) 来设计高效、有效和鲁棒的元启发式的研究变得越来越流行。许多机器学习支持的元启发式算法已经产生了高质量的结果，并代表了最先进的优化算法。尽管已经提出了各种方法，但缺乏对该研究主题的全面调查和分类。在本文中，我们将研究将 ML 用于元启发式的不同机会。我们统一定义可能实现协同效应的各种方式。根据相关搜索组件提出了详细的分类：目标优化问题以及元启发式的低级和高级组件。我们的目标也是激励优化研究人员将 ML 的想法纳入元启发式算法。我们确定了该主题中一些需要进一步深入调查的开放研究问题。