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.
@article{arxiv.1604.00377,
title = {Reinforcement learning based local search for grouping problems: A case study on graph coloring},
author = {Yangming Zhou and Jin-Kao Hao and Béatrice Duval},
journal= {arXiv preprint arXiv:1604.00377},
year = {2016}
}