A fundamental but largely unaddressed obstacle in Symbolic regression (SR) is structural redundancy: every expression DAG with admits many distinct node-numbering schemes that all encode the same expression, each occupying a separate point in the search space and consuming fitness evaluations without adding diversity. We present IsalSR (Instruction Set and Language for Symbolic Regression), a representation framework that encodes expression DAGs as strings over a compact two-tier alphabet and computes a pruned canonical string -- a complete labeled-DAG isomorphism invariant -- that collapses all the equivalent representations into a single canonical form.
Cite
@article{arxiv.2603.21836,
title = {Instruction Set and Language for Symbolic Regression},
author = {Ezequiel Lopez-Rubio and Mario Pascual-Gonzalez},
journal= {arXiv preprint arXiv:2603.21836},
year = {2026}
}