Evolutionary computation (EC), as a powerful optimization algorithm, has been applied across various domains. However, as the complexity of problems increases, the limitations of EC have become more apparent. The advent of large language models (LLMs) has not only transformed natural language processing but also extended their capabilities to diverse fields. By harnessing LLMs' vast knowledge and adaptive capabilities, we provide a forward-looking overview of potential improvements LLMs can bring to EC, focusing on the algorithms themselves, population design, and additional enhancements. This presents a promising direction for future research at the intersection of LLMs and EC.

翻译：演化计算（EC）作为一种强大的优化算法，已被广泛应用于多个领域。然而，随着问题复杂度的增加，EC的局限性愈发明显。大语言模型（LLMs）的出现不仅改变了自然语言处理领域，更将其能力拓展至多个学科。通过利用LLMs丰富的知识库与自适应能力，本文前瞻性地综述了LLMs可为EC带来的潜在改进，重点关注算法本身、种群设计及额外增强等层面。这为未来LLMs与EC交叉领域的研究指明了富有前景的方向。