In this survey, we introduce Meta-Black-Box-Optimization (MetaBBO) as an emerging avenue within the Evolutionary Computation (EC) community, which incorporates Meta-learning approaches to assist automated algorithm design. Despite the success of MetaBBO, the current literature provides insufficient summaries of its key aspects and lacks practical guidance for implementation. To bridge this gap, we offer a comprehensive review of recent advances in MetaBBO, providing an in-depth examination of its key developments. We begin with a unified definition of the MetaBBO paradigm, followed by a systematic taxonomy of various algorithm design tasks, including algorithm selection, algorithm configuration, solution manipulation, and algorithm generation. Further, we conceptually summarize different learning methodologies behind current MetaBBO works, including reinforcement learning, supervised learning, neuroevolution, and in-context learning with Large Language Models. A comprehensive evaluation of the latest representative MetaBBO methods is then carried out, alongside an experimental analysis of their optimization performance, computational efficiency, and generalization ability. Based on the evaluation results, we meticulously identify a set of core designs that enhance the generalization and learning effectiveness of MetaBBO. Finally, we outline the vision for the field by providing insight into the latest trends and potential future directions. Relevant literature will be continuously collected and updated at https://github.com/GMC-DRL/Awesome-MetaBBO.

翻译：本综述将元黑盒优化（MetaBBO）作为进化计算（EC）领域的新兴研究方向进行介绍，该方法融合元学习技术以辅助自动化算法设计。尽管MetaBBO已取得显著进展，现有文献对其核心要点的总结尚不充分，且缺乏可操作的实践指导。为弥补这一空白，本文对MetaBBO的最新进展进行全面梳理，深入剖析其关键发展脉络。我们首先提出MetaBBO范式的统一定义，进而系统性地对各类算法设计任务进行分类，包括算法选择、算法配置、解空间操作及算法生成。进一步，我们从概念层面归纳当前MetaBBO研究背后的不同学习范式，涵盖强化学习、监督学习、神经进化以及基于大语言模型的上下文学习。随后对最具代表性的前沿MetaBBO方法展开综合评估，并通过实验分析其优化性能、计算效率与泛化能力。基于评估结果，我们精准提炼出一系列能提升MetaBBO泛化性能与学习效能的核心设计原则。最后，通过剖析最新趋势与潜在发展方向，我们展望了该领域的未来愿景。相关文献将持续收集并更新于https://github.com/GMC-DRL/Awesome-MetaBBO。