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.
@article{arxiv.2405.02876,
title = {Exploring the Improvement of Evolutionary Computation via Large Language Models},
author = {Jinyu Cai and Jinglue Xu and Jialong Li and Takuto Ymauchi and Hitoshi Iba and Kenji Tei},
journal= {arXiv preprint arXiv:2405.02876},
year = {2024}
}