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New research explores gray-box optimization for graph coloring

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore the application of local search and gray-box optimization techniques to the vertex coloring problem, particularly on bipartite graphs. The research identifies specific graph structures that can lead local search algorithms to suboptimal solutions. However, by introducing gray-box operators that leverage problem-specific information, such as removing less frequent colors, researchers demonstrated significant improvements in finding optimal colorings, reducing expected run times from exponential to polynomial. AI

IMPACT These papers advance theoretical understanding of optimization algorithms, potentially leading to more efficient AI approaches for problems involving graph structures.

RANK_REASON The cluster contains two academic papers detailing novel research on optimization algorithms for graph coloring.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research explores gray-box optimization for graph coloring

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The cluster contains two academic papers detailing novel research on optimization algorithms for graph coloring.
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COVERAGE [2]

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

    Local Search on Vertex Coloring for Bipartite Graphs

    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 ·

    Gray-Box Optimization and the Vertex Coloring Problem

    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…

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