Large Language Models (LLMs), built upon Transformer-based architectures with massive pretraining on diverse data, have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artificial general intelligence. The interplay between LLMs and Evolutionary Algorithms (EAs), despite differing in objectives and methodologies, reveals intriguing parallels, especially in their shared optimization nature, black-box characteristics, and proficiency in handling complex problems. Meanwhile, EA can not only provide an optimization framework for LLM's further enhancement under black-box settings but also empower LLM with flexible global search and iterative mechanism in applications. On the other hand, LLM's abundant domain knowledge enables EA to perform smarter searches, while its text processing capability assist in deploying EA across various tasks. Based on their complementary advantages, this paper presents a comprehensive review and forward-looking roadmap, categorizing their mutual inspiration into LLM-enhanced evolutionary optimization and EA-enhanced LLM. Some integrated synergy methods are further introduced to exemplify the amalgamation of LLMs and EAs in various application scenarios, including neural architecture search, code generation, software engineering, and text generation. As the first comprehensive review specifically focused on the EA research in the era of LLMs, this paper provides a foundational stepping stone for understanding and harnessing the collaborative potential of LLMs and EAs. By presenting a comprehensive review, categorization, and critical analysis, we contribute to the ongoing discourse on the cross-disciplinary study of these two powerful paradigms. The identified challenges and future directions offer guidance to unlock the full potential of this innovative collaboration.

翻译：大语言模型（LLMs）基于Transformer架构，通过海量多样数据的预训练构建而成，不仅彻底革新了自然语言处理领域，还将其能力拓展至多个领域，标志着向通用人工智能迈出了重要一步。尽管LLMs与进化算法（EAs）在目标和方 methodologies 上存在差异，但两者之间的相互作用揭示了有趣的相似性，特别是在共同优化本质、黑箱特性以及处理复杂问题的能力方面。同时，EA不仅能作为优化框架，在黑箱设定下进一步增强LLM的性能，还能赋予LLM在应用中灵活进行全局搜索和迭代机制的能力。另一方面，LLM丰富的领域知识使EA能够进行更智能的搜索，而其文本处理能力则有助于EA在不同任务中的部署。基于两者的互补优势，本文提出了全面的综述和前瞻性路线图，将它们的相互启发分为LLM增强进化优化和EA增强LLM两类。进一步介绍了部分融合协同方法，以示例说明LLM与EA在多种应用场景中的结合，包括神经架构搜索、代码生成、软件工程和文本生成。作为首篇专门聚焦于LLM时代EA研究的全面综述，本文为理解和利用LLM与EA的协作潜力奠定了基石。通过全面的梳理、分类和批判性分析，我们为这两种强大范式跨学科研究的持续讨论做出了贡献。所识别的挑战和未来方向为释放这一创新合作的全部潜力提供了指导。