Exploring the Improvement of Evolutionary Computation via Large Language Models

• URL: http://arxiv.org/abs/2405.02876v2
• Date: Thu, 23 May 2024 10:10:11 GMT
• Title: Exploring the Improvement of Evolutionary Computation via Large Language Models
• Authors: Jinyu Cai, Jinglue Xu, Jialong Li, Takuto Ymauchi, Hitoshi Iba, Kenji Tei,
• Abstract summary: Evolutionary computation (EC) has been applied across various domains. As the complexity of problems increases, the limitations of EC have become more apparent. By harnessing large language models' vast knowledge and adaptive capabilities, we provide a forward-looking overview of potential improvements.
• Score: 3.4641800438055297
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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