From Overparameterised Expressions to Parsimonious Models: Fisher-Geometric Pruning for Symbolic Regression
Genetic programming symbolic regression (GPSR) often returns expressions that are larger than necessary, with dormant branches and finely tuned algebraic configurations that fit sampling noise. Existing complexity-control strategies, such as multi-objective size-accuracy search or information criteria embedded in the fitness function, act on syntactic representations during model search. We introduce Fisher-Geometric pruning (FGP), a post-hoc model-reduction framework that acts on fitted models and can complement these techniques. FGP combines local information geometry at the maximum-likelihood solution with explicit compatibility tests for interpretable parametric boundaries. A taxonomy...
| Authors | Veronica Guidetti, Luca La Rocca, Fabrício Olivetti de França et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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