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English(EN) Gray-Box Optimization and the Vertex Coloring Problem

新研究探索用于图着色的灰盒优化

两篇新研究论文探讨了局部搜索和灰盒优化技术在顶点着色问题上的应用,特别是在二分图上。研究确定了可能导致局部搜索算法得出次优解的特定图结构。然而,通过引入利用问题特定信息的灰盒算子(例如移除不常用颜色),研究人员展示了在寻找最优着色方面取得的显著改进,将预期运行时间从指数级降低到多项式级。 AI

影响 这些论文推进了对优化算法的理论理解,可能为涉及图结构的更高效的AI方法带来新的可能。

排序理由 该集群包含两篇学术论文,详细介绍了图着色优化算法的新研究。

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

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新研究探索用于图着色的灰盒优化

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该集群包含两篇学术论文,详细介绍了图着色优化算法的新研究。
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报道来源 [2]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Johanna Gasse ·

    二分图的顶点着色局部搜索

    Local search is a well-known heuristic method used in optimization. In this thesis, we explore its capabilities on the vertex coloring problem, an $NP$-hard problem with relevance in both theoretical analysis and practical application. To recognize limitations in the applicabilit…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Timo Kötzing ·

    灰盒优化与顶点着色问题

    Gray-box optimization is an approach for making some problem-specific information available to the algorithm while still relying on fitness information as the main guide to an optimum. This approach was shown to be beneficial in various combinatorial optimization tasks and neatly…