AI RESEARCH
A Comparative Study of Model Selection Criteria for Symbolic Regression
arXiv CS.LG
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ArXi:2605.11233v1 Announce Type: new Effective model selection is critical in symbolic regression (SR) to identify mathematical expressions that balance accuracy and complexity, and have low expected error on unseen data. Many modern implementations of genetic programming (GP) for SR generate a set of Pareto optimal candidate solutions, but reliable automatic selection of solutions that generalize well remains an open issue.