LLM-Meta-SR: In-Context Learning for Evolving Selection Operators in Symbolic Regression

ArXi:2505.18602v3 Announce Type: replace-cross Large language models (LLMs) have revolutionized algorithm development, yet their application in symbolic regression, where algorithms automatically discover symbolic expressions from data, remains limited. In this paper, we propose a meta-learning framework that enables LLMs to automatically design selection operators for evolutionary symbolic regression algorithms. We first identify two key limitations in existing LLM-based algorithm evolution techniques: lack of semantic guidance and code bloat.