新研究探索用于符号回归的遗传编程 · 已追踪 2 个来源

报道来源 [4]

1. arXiv cs.LG TIER_1 English(EN) · Lukas Kammerer, Gabriel Kronberger, Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira, Stephan Winkler · 2026-07-01 04:00

   面向基于遗传编程的符号回归的种群初始化方法评估

   arXiv:2606.31990v1 Announce Type: cross Abstract: We analyze the effect of optimizing the initial population of genetic programming (GP) for symbolic regression (SR) on the accuracy and complexity of solutions. We compare three well-established random initialization methods as we…

2. arXiv cs.LG TIER_1 English(EN) · Stephan Winkler · 2026-06-30 17:28

   基于遗传编程的符号回归的种群初始化方法评估

   We analyze the effect of optimizing the initial population of genetic programming (GP) for symbolic regression (SR) on the accuracy and complexity of solutions. We compare three well-established random initialization methods as well as initialization with small optimized solution…

3. arXiv cs.AI TIER_1 English(EN) · Artem Andrianov (Cyntegrity Germany GmbH, Hofheim am Taunus, Germany) · 2026-06-30 04:00

   演化数学：用于小型、宽数据集可解释符号回归的交叉验证岛屿模型遗传编程

   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 …

4. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Artem Andrianov · 2026-06-20 10:31

   演化数学：交叉验证的岛屿模型遗传编程，用于小而宽数据集的可解释符号回归

   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. We pre…