探索自动化算法设计 协同大型语言模型和进化算法:调查和见解
1School of Artificial Intelligence, Xidian University, 2 South Taibai Road, Xi'an, Shaanxi 710071, China yuhe001@stu.xidian.edu.cn.
Evolutionary computation
|October 31, 2025
概括
本研究介绍了一种范式,将大型语言模型 (LLM) 与进化算法 (EA) 集成为自动化优化算法设计. 这种协同作用提高了优化策略的效率和创造力.
科学领域:
- 人工智能的人工智能
- 计算优化计算优化
- 算法设计 算法设计
背景情况:
- 传统的优化算法严重依赖于手动设计和领域专业知识,限制了可扩展性.
- 进化算法 (EA) 提供了复杂的搜索空间的高效探索.
- 大型语言模型 (LLM) 可以作为战略生成和改进的动态代理.
研究的目的:
- 为自动化优化算法设计提出和分析一个整合LLMs和EA的新型范式.
- 探索LLM如何增强EA的关键模块,包括代表性,选择性,变异性和适应性评估.
- 调查LLM提示在适应和指导进化过程中的作用.
主要方法:
- 对现有的LLM-EA整合发展进行系统审查.
- 对EA模块设计的LLM提示工程进行深入分析.
- 检查LLM驱动的语义智能在EA的特征,如多样性和融合.
主要成果:
- LLM-EA的协同作用使得更自动化,更高效和更有创意的优化算法设计成为可能.
- 基于进化反的快速进化可以动态调整优化策略.
- 法律法规引入语义智能,提高EA的适应性和可扩展性.
结论:
- 该LLM-EA范式代表了自动化优化中的重大进步.
- 这种方法解决了手动设计的局限性,并增强了EA的核心功能.
- 鼓励在该领域进行进一步的研究,以促进自动化算法开发.
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