DASS: A Solver-Adaptable Dynamic Auxiliary Search Strategy for Symbolic Regression
DASS introduces a dynamic auxiliary search strategy for symbolic regression to navigate complex mathematical expression spaces efficiently. It creates and continuously refines a high-potential subspace of basis terms, using feedback from solvers to guide the search toward promising regions. This method significantly improves accuracy while maintaining manageable runtime increases by steering the search away from undirected trial-and-error.