Large Language Models (LLMs) 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, share a common pursuit of applicability in complex problems. Meanwhile, EA can provide an optimization framework for LLM's further enhancement under black-box settings, empowering LLM with flexible global search capacities. On the other hand, the abundant domain knowledge inherent in LLMs could enable EA to conduct more intelligent searches. Furthermore, the text processing and generative capabilities of LLMs would aid in deploying EAs across a wide range of tasks. Based on these complementary advantages, this paper provides a thorough review and a forward-looking roadmap, categorizing the reciprocal inspiration into two main avenues: LLM-enhanced EA and EA-enhanced LLM. Some integrated synergy methods are further introduced to exemplify the amalgamation of LLMs and EAs in diverse scenarios, including neural architecture search, code generation, software engineering, and various generation tasks. As the first comprehensive review focused on the EA research in the era of LLMs, this paper provides a foundational stepping stone for understanding the collaborative potential of LLMs and EAs. By meticulous 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 for researchers and practitioners aiming to unlock the full potential of this innovative collaboration in propelling advancements in optimization and artificial intelligence.

翻译：大语言模型（LLMs）不仅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）在目标和方法上存在差异，但它们在复杂问题中的适用性追求是共通的。同时，EA可为LLMs在黑箱设置下的进一步优化提供框架，赋予其灵活的全域搜索能力。另一方面，LLMs蕴含的丰富领域知识能使EA进行更智能的搜索。此外，LLMs的文本处理与生成能力有助于EA在广泛任务中的部署。基于这些互补优势，本文进行了全面综述并提出了前瞻性路线图，将二者的相互启发分为两大方向：LLM增强的EA和EA增强的LLM。进一步介绍了若干集成协同方法，以示例说明LLMs与EAs在多种场景下的融合，包括神经架构搜索、代码生成、软件工程及各类生成任务。作为首篇聚焦于LLM时代EA研究的全面综述，本文为理解LLMs与EAs的协作潜力奠定了坚实基础。通过细致的分类与批判性分析，我们为这两种强大范式的跨学科研究提供了持续探讨的贡献。识别的挑战与未来方向为旨在释放这一创新协作在优化与人工智能进步中全部潜力的研究人员与实践者提供了指导。