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 complementarity between LLMs and EAs in diverse scenarios, including code generation, software engineering, neural architecture search, 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. The identified challenges and future directions offer guidance for researchers and practitioners to unlock the full potential of this innovative collaboration in propelling advancements in optimization and artificial intelligence. We have created a GitHub repository to index the relevant papers: https://github.com/wuxingyu-ai/LLM4EC.

翻译：大语言模型（LLMs）不仅彻底改变了自然语言处理领域，更将其强大能力扩展至众多其他领域，标志着向通用人工智能迈出了重要一步。尽管大语言模型与进化算法（EAs）在目标和方法上存在差异，但二者在复杂问题适用性方面有着共同的追求。一方面，进化算法能在黑盒设置下为大语言模型的进一步优化提供框架，赋予大语言模型灵活的全局搜索能力。另一方面，大语言模型内蕴的丰富领域知识可使进化算法进行更智能的搜索。此外，大语言模型的文本处理与生成能力将有助于进化算法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，将二者的相互启发归纳为两大主线：LLM增强的EA与EA增强的LLM。文中进一步介绍了一些综合协同方法，以例证大语言模型与进化算法在代码生成、软件工程、神经架构搜索及各类生成任务等多样化场景中的互补性。作为首篇聚焦大语言模型时代进化计算研究的全面综述，本文为理解大语言模型与进化算法的协作潜力提供了基础性阶梯。文中指出的挑战与未来方向为研究者和实践者提供了指引，以充分释放这一创新协作在推动优化与人工智能进步方面的潜力。我们已创建GitHub仓库以索引相关论文：https://github.com/wuxingyu-ai/LLM4EC。