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

1. 1Identify the limitations of existing LLM-based algorithm evolution approaches
2. 2Design a meta-learning framework to enable LLMs to learn selection operators
3. 3Implement the LLM-Meta-SR framework to automate the design of selection operators for evolutionary symbolic regression algorithms
4. 4Evaluate the performance of the proposed framework on benchmark datasets

Machine learning researchers and engineers working on symbolic regression can benefit from this framework to improve the efficiency of their algorithms, while data scientists can apply the proposed method to automate the discovery of symbolic expressions from data

💡 The proposed framework enables large language models to automatically design selection operators for evolutionary symbolic regression algorithms, improving their efficiency and effectiveness

🤖 LLM-Meta-SR: A new framework for evolving selection operators in symbolic regression using large language models! 💻