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：进化计算（EC）作为一种强大的优化算法，已在多个领域得到应用。然而，随着问题复杂度的增加，EC的局限性日益凸显。大型语言模型（LLMs）的出现不仅变革了自然语言处理领域，更将其能力扩展至众多其他领域。通过利用LLMs的广博知识与自适应能力，本文前瞻性地概述了LLMs可能为EC带来的改进，重点关注算法本身、种群设计以及其他增强方向。这为LLMs与EC交叉领域的未来研究提供了一个充满前景的方向。