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English(EN) Efficient Constant Optimization for Symbolic Regression with GPU-Accelerated Tree-Based Genetic Programming

新的GPU求解器加速符号回归常数优化

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的GPU常驻求解器，用于通过基于树的遗传编程优化符号回归中的常数。这种批量Levenberg-Marquardt求解器能够以每次迭代最少的CUDA启动次数，高效地处理异构表达式树种群。它利用反向模式自动微分来组装雅可比矩阵，并采用双精度保护来确保常数质量，在NVIDIA A100硬件上实现了高吞吐量，并优于现有方法。 AI

影响 这项研究可能有助于在科学建模和AI开发中更有效地发现控制方程。

排序理由 该集群包含一篇学术论文，详细介绍了一种用于特定计算任务的新方法和实现。

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的GPU求解器加速符号回归常数优化

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Hao Mao, Xu Tony Liu, Shuai Lu, Peng Zhao, Wenzheng Jiang, Yuntian Chen ·

    使用GPU加速的基于树的遗传编程进行符号回归的高效常数优化

    arXiv:2609.03352v1 Announce Type: cross Abstract: Constant optimization refines the numerical coefficients of candidate expressions in tree-based genetic programming for symbolic regression. But its per-generation cost has led modern GPU-accelerated frameworks to omit it or restr…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Yuntian Chen ·

    面向符号回归的高效常数优化，采用GPU加速的基于树的遗传编程

    Constant optimization refines the numerical coefficients of candidate expressions in tree-based genetic programming for symbolic regression. But its per-generation cost has led modern GPU-accelerated frameworks to omit it or restrict it to lightweight forms. We present a GPU-resi…

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