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• W2952376875 abstract "Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints. Grouping problems cover a large class of important combinatorial optimization problems that are generally computationally difficult. In this paper, we propose a general solution approach for grouping problems, i.e., reinforcement learning based local search (RLS), which combines reinforcement learning techniques with descent-based local search. The viability of the proposed approach is verified on a well-known representative grouping problem (graph coloring) where a very simple descent-based coloring algorithm is applied. Experimental studies on popular DIMACS and COLOR02 benchmark graphs indicate that RLS achieves competitive performances compared to a number of well-known coloring algorithms." @default.
• W2952376875 created "2019-06-27" @default.
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• W2952376875 date "2016-04-01" @default.
• W2952376875 modified "2023-09-27" @default.
• W2952376875 title "Reinforcement learning based local search for grouping problems: A case study on graph coloring" @default.
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• W2952376875 doi "https://doi.org/10.48550/arxiv.1604.00377" @default.
• W2952376875 hasPublicationYear "2016" @default.
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