Metaheuristic algorithms have attracted wide attention from academia and industry due to their capability of conducting search independent of problem structures and problem domains. Often, human experts are requested to manually tailor algorithms to fit for solving a targeted problem. The manual tailoring process may be laborious, error-prone, and require intensive specialized knowledge. This gives rise to increasing interests and demands for automated design of metaheuristic algorithms with less human intervention. The automated design could make high-performance algorithms accessible to a much broader range of researchers and practitioners; and by leveraging computing power to fully explore the potential design choices, automated design could reach or even surpass human-level design. This paper presents a broad picture of the formalization, methodologies, challenges, and research trends of automated design of metaheuristic algorithms, by conducting a survey on the common grounds and representative techniques in this field. In the survey, we first present the concept of automated design of metaheuristic algorithms and provide a taxonomy by abstracting the automated design process into four parts, i.e., design space, design strategies, performance evaluation strategies, and targeted problems. Then, we overview the techniques concerning the four parts of the taxonomy and discuss their strengths, weaknesses, challenges, and usability, respectively. Finally, we present research trends in this field.

翻译：元启发式算法因其能够独立于问题结构和问题领域进行搜索的能力，已引起学术界和工业界的广泛关注。通常，需要人类专家手动调整算法以适配特定问题的求解。这种手动调整过程可能费时费力、容易出错，且需要大量专业知识。这促使对减少人工干预的元启发式算法自动化设计产生了日益增长的兴趣和需求。自动化设计能使更广泛的研究人员和实践者获得高性能算法；同时，通过借助计算能力充分探索潜在的设计选择，自动化设计能够达到甚至超越人类设计水平。本文通过调研该领域的共同基础与代表性技术，全面描绘了元启发式算法自动化设计的形式化表达、方法论、挑战及研究趋势。在调研中，我们首先阐述了元启发式算法自动化设计的概念，并通过将自动化设计过程抽象为四个部分（即设计空间、设计策略、性能评估策略和目标问题）提出了分类体系。接着，我们概述了涉及分类体系中四个部分的技术，并分别讨论了它们的优势、局限性、挑战及可用性。最后，我们呈现了该领域的研究趋势。