Neural Structure Embedding for Symbolic Regression via Continuous Structure Search and Coefficient Optimization
Symbolic regression aims to discover human-interpretable equations that explain observational data. However, existing approaches rely heavily on discrete structure search (e.g., genetic programming), which often leads to high computational cost, unstable performance, and limited scalability to large equation spaces. To address these challenges, we propose SRCO, a unified embedding-driven framework for symbolic regression that transforms symbolic structures into a continuous, optimizable representation space. The framework consists of three key components: (1) structure embedding: we first generate a large pool of exploratory equations using traditional symbolic regression algorithms and t...
| Authors | Fateme Memar, Tao Zhe, Dongjie Wang |
|---|---|
| Primary category | cs.LG |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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