  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. By comprehending the code text, LLM not
only captures the structural and semantic aspects of the algorithm, but also
demonstrates contextual awareness and library function understanding. The
high-dimensional algorithm representation extracted by LLM, after undergoing a
feature selection module, is combined with the problem representation and
passed to the similarity calculation module. The selected algorithm is
determined by the matching degree between a given problem and different
algorithms. Extensive experiments validate the performance superiority of the
proposed model and the efficacy of each key module. Furthermore, we present a
theoretical upper bound on model complexity, showcasing the influence of
algorithm representation and feature selection modules. This provides valuable
theoretical guidance for the practical implementation of our method.
