Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 64, Issue -, Pages 412-422Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2016.07.047
Keywords
Grouping problems; Reinforcement learning; Heuristics; Learning-based optimization
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Funding
- PGMO project (Jacques Hadamard Mathematical Foundation, Paris)
- China Scholarship Council (CSC)
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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 computational problems including clustering and classification that frequently appear in expert and intelligent systems as well as many real applications. This paper focuses on developing a general-purpose solution approach for grouping problems, i.e., reinforcement learning based local search (RLS), which combines reinforcement learning techniques with local search. This paper makes the following contributions: we show that (1) reinforcement learning can help obtain useful information from discovered local optimum solutions; (2) the learned information can be advantageously used to guide the search algorithm towards promising regions. To the best of our knowledge, this is the first attempt to. propose a formal model that combines reinforcement learning and local search for solving grouping problems. The proposed approach is verified on a well-known representative grouping problem (graph coloring). The generality of the approach makes it applicable to other grouping problems. (C) 2016 Elsevier Ltd. All rights reserved.
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