Abstract
Integrating Large Language Models (LLMs) and Evolutionary Computation (EC) represents a promising avenue for advancing artificial intelligence by combining powerful natural language understanding with optimization and search capabilities. This manuscript explores the synergistic potential of LLMs and EC, reviewing their intersections, complementary strengths, and emerging applications. We identify key opportunities where EC can enhance LLM training, fine-tuning, prompt engineering, and architecture search, while LLMs can, in turn, aid in automating the design, analysis, and interpretation of ECs. The manuscript explores the synergistic integration of EC and LLMs, highlighting their bidirectional contributions to advancing artificial intelligence. It first examines how EC techniques enhance LLMs by optimizing key components such as prompt engineering, hyperparameter tuning, and architecture search, demonstrating how evolutionary methods automate and refine these processes. Secondly, the survey investigates how LLMs improve EC by automating metaheuristic design, tuning evolutionary algorithms, and generating adaptive heuristics, thereby increasing efficiency and scalability. Emerging co-evolutionary frameworks are discussed, showcasing applications across diverse fields while acknowledging challenges like computational costs, interpretability, and algorithmic convergence. The survey concludes by identifying open research questions and advocating for hybrid approaches that combine the strengths of EC and LLMs.
Abstract (translated)
将大型语言模型（LLM）和进化计算（EC）结合在一起代表了一种有前景的方法，可以通过将强大的自然语言理解与优化和搜索能力相结合来推进人工智能的发展。本文探讨了LLM与EC之间的协同潜力，回顾了它们的交集、互补优势以及新兴应用。我们确定了一些关键机遇，其中EC可以增强LLM的训练、微调、提示工程及架构搜索，而LLM则可以在设计自动化、分析和解释EC方面提供帮助。论文探索了EC和LLM的相互集成方式，强调了它们在推进人工智能方面的双向贡献。首先，本文考察了EC技术如何通过优化关键组件（如提示工程、超参数调优和架构搜索）来增强LLM，展示了进化方法是如何自动化并改进这些过程的。其次，文献调查了LLM如何通过自动设计元启发式算法、调整进化算法以及生成自适应启发法来提高EC的效率和可扩展性。本文还讨论了一些新兴的共生框架，展示它们在各个领域的应用，并且注意到了诸如计算成本、解释性和算法收敛等挑战。最终，文献确定了开放性的研究问题，并倡导采用结合了EC和LLM优势的混合方法。
URL
https://arxiv.org/abs/2505.15741
