Instruction Set and Language for Symbolic Regression
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915883177213952 |
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| author | Lopez-Rubio, Ezequiel Pascual-Gonzalez, Mario |
| author_facet | Lopez-Rubio, Ezequiel Pascual-Gonzalez, Mario |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21836 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Instruction Set and Language for Symbolic Regression Lopez-Rubio, Ezequiel Pascual-Gonzalez, Mario Computation and Language Artificial Intelligence Programming Languages 68T10 I.2 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. |
| title | Instruction Set and Language for Symbolic Regression |
| topic | Computation and Language Artificial Intelligence Programming Languages 68T10 I.2 |
| url | https://arxiv.org/abs/2603.21836 |