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English(EN) Interpretable Cutting Plane Selection for Mixed-Integer Linear Programming Using Genetic Programming

遗传编程技术增强MILP求解器并自动化ML模型分配

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文探讨了遗传编程技术在增强机器学习模型中的应用。第一篇论文介绍了用于混合整数线性规划（MILP）求解器的基于遗传编程和统计的割选择（GPSCS），证明了求解时间和泛化能力的提高。第二篇论文提出了强化线性遗传编程（RLGP），它将Q学习与线性遗传编程相结合，以自动化寄存器-动作分配，并引入了一个新的基于Rust的实验框架。 AI

影响 这些论文提出了提高机器学习模型效率和可解释性的新方法，可能影响优化和自动化系统设计等领域。

排序理由 两篇arXiv论文详细介绍了遗传编程在机器学习中的新应用。

在 arXiv cs.LG 阅读 →

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

遗传编程技术增强MILP求解器并自动化ML模型分配

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Gwen maudet, Mathis Da Cunha, Gr\'egoire Danoy ·

    Interpretable Cutting Plane Selection for Mixed-Integer Linear Programming Using Genetic Programming

    arXiv:2610.29311v1 Announce Type: new Abstract: Cutting planes (cuts) remove fractional solutions from mixed-integer linear programs (MILPs) without excluding any feasible integer solutions, making them a key ingredient in modern MILP solvers. Current state-of-the-art solvers rel…

  2. arXiv cs.AI TIER_1 English(EN) · Urmzd Mukhammadnaim ·

    强化线性遗传编程

    arXiv:2601.09736v2 Announce Type: replace-cross Abstract: Linear Genetic Programming (LGP) is a powerful technique that allows for a variety of problems to be solved using a linear representation of programs. However, there still exists some limitations to the technique, such as …

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