arXiv:2604. 17402v2 Announce Type: replace Abstract: Symbolic regression (SR) with genetic programming (GP) aims to discover interpretable mathematical expressions directly from data.
By Masahiro Nomura, Ryoki Hamano, Isao Ono
arXiv:2606. 28381v1 Announce Type: cross Abstract: Symbolic regression via genetic programming routinely fails on small, wide datasets - a regime common in clinical-trial monitoring, biostatistics, and engineering pilot studies - by converging on bloated, overfit expressions that exploit correlation rather than prediction.
By Artem Andrianov (Cyntegrity Germany GmbH, Hofheim am Taunus, Germany)
arXiv:2606. 10237v1 Announce Type: new Abstract: Genetic programming (GP) is based on two important insights.
By Leonardo Trujillo
The paper introduces a GPU-resident, batched Levenberg–Marquardt solver that efficiently optimizes constants in tree-based genetic programming for symbolic regression. By using reverse-mode automatic differentiation to assemble per-tree Jacobians in a single backward sweep, the solver’s per-iteration cost becomes independent of the number of constants per tree, achieving up to 510,000 trees per second on an NVIDIA A100. Integrated into EvoGP, the solver enables end-to-end search that recovers governing equations on 10 of 18 constructed problems, a significant improvement over stock EvoGP.
By Hao Mao, Xu Tony Liu, Shuai Lu, Peng Zhao, Wenzheng Jiang, Yuntian Chen
arXiv:2511. 04124v3 Announce Type: replace Abstract: Symbolic regression (SR) models complex systems by discovering mathematical expressions that capture underlying relationships in observed data.
By Giorgio Morales, John W. Sheppard
arXiv:2606. 15923v1 Announce Type: cross Abstract: Cartesian Genetic Programming (CGP) is among the practical and popular forms of Genetic Programming as it uses a graph-based representation of programs.
By Duc-Cuong Dang, Roman Kalkreuth, Andre Opris