Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap
CoRR(2024)
摘要
Large Language Models (LLMs), built upon Transformer-based architectures with
massive pretraining on diverse data, have not only revolutionized natural
language processing but also extended their prowess to various domains, marking
a significant stride towards artificial general intelligence. The interplay
between LLMs and Evolutionary Algorithms (EAs), despite differing in objectives
and methodologies, reveals intriguing parallels, especially in their shared
optimization nature, black-box characteristics, and proficiency in handling
complex problems. Meanwhile, EA can not only provide an optimization framework
for LLM's further enhancement under black-box settings but also empower LLM
with flexible global search and iterative mechanism in applications. On the
other hand, LLM's abundant domain knowledge enables EA to perform smarter
searches, while its text processing capability assist in deploying EA across
various tasks. Based on their complementary advantages, this paper presents a
comprehensive review and forward-looking roadmap, categorizing their mutual
inspiration into LLM-enhanced evolutionary optimization and EA-enhanced LLM.
Some integrated synergy methods are further introduced to exemplify the
amalgamation of LLMs and EAs in various application scenarios, including neural
architecture search, code generation, software engineering, and text
generation. As the first comprehensive review specifically focused on the EA
research in the era of LLMs, this paper provides a foundational stepping stone
for understanding and harnessing the collaborative potential of LLMs and EAs.
By presenting a comprehensive review, categorization, and critical analysis, we
contribute to the ongoing discourse on the cross-disciplinary study of these
two powerful paradigms. The identified challenges and future directions offer
guidance to unlock the full potential of this innovative collaboration.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要