Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation

Understand

Algorithm selection, a critical process of automated machine learning, aims to identify the most suitable algorithm for solving a specific problem prior to execution.

• Mainstream algorithm selection techniques heavily rely on problem features, while the role of algorithm features remains largely unexplored.
• Due to the intrinsic complexity of algorithms, effective methods for universally extracting algorithm information are lacking.
• This paper takes a significant step towards bridging this gap by introducing Large Language Models (LLMs) into algorithm selection for the first time.