A BRKGA-based matheuristic for the maximum quasi-clique problem with an exact local search strategy

Given a graph G = ( V, E ) and threshold ? ? (0, 1], the maximum cardinality quasi- clique problem consists in finding subset C * of vertices V such that density induced by is greater than or equal to ?. This has number applications data mining, e.g ., social networks phone call graphs. We propose matheuristic for solving quasi-clique problem, based on hybridization biased random-key genetic algorithm (BRKGA) with an exact local search strategy. The newly proposed approach compared pure algorithm, which was best heuristic literature at time writing. Computational results show hybrid BRKGA outperforms BRKGA.