Opposition-based learning memetic algorithm for the maximum intersection of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si5.svg" display="inline" id="d1e1728"> <mml:mi>k</mml:mi> </mml:math> -subsets problem

Given m elements and n subsets of elements, the maximum intersection of k -subsets (kMIS) problem is to select k subsets of elements to maximize the number of elements simultaneously covered by all of the selected subsets. As a general model, kMIS can be used to formulate some practical problems including data privacy control, community detection, and deoxyribonucleic acid microarray technology. This paper presents an opposition-based learning memetic algorithm that integrates opposition-based learning initialization, adaptive crossover, and solution-based tabu search. Experimental results on 608 instances show that the algorithm competes favorably with the state-of-the-art methods. The importance of the algorithmic components is experimentally validated.