Exploring the Improvement of Evolutionary Computation via Large Language Models

TL;DR

This paper explores how large language models can enhance evolutionary computation by improving algorithms, population design, and other aspects, opening new research directions at this interdisciplinary intersection.

Contribution

It provides a forward-looking overview of integrating LLMs into EC, highlighting potential improvements and future research opportunities.

Findings

LLMs can enhance EC algorithms and population strategies.

Potential for LLMs to address EC limitations in complex problems.

Future research directions at the intersection of LLMs and EC.

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.