Abstract
In this work, we consider the following NP-hard combinatorial optimization problem from computational biology. Given a set of input strings of equal length, the goal is to identify a maximum cardinality subset of strings that differ maximally in a pre-defined number of positions. First of all, we introduce an integer linear programming model for this problem. Second, two variants of a rather simple greedy strategy are proposed. Finally, a large neighborhood search algorithm is presented. A comprehensive experimental comparison among the proposed techniques shows, first, that larger neighborhood search generally outperforms both greedy strategies. Second, while large neighborhood search shows to be competitive with the stand-alone application of CPLEX for small- and medium-sized problem instances, it outperforms CPLEX in the context of larger instances.
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Acknowledgments
All experiments were executed in the High Performance Cluster managed by the Research and Development Lab (RDlab) of the Computer Science Dept. at the Universitat Politècnica de Catalunya (http://rdlab.cs.upc.edu). We thank all the RDlab staff for their support. A preliminary version of this work appeared at the IEEE 2015 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), September 2–4, 2015, Madrid, Spain. This work was supported by project TIN2012-37930-C02-02 (Spanish Ministry for Economy and Competitiveness, FEDER funds from the European Union) and project SGR 2014-1034 (AGAUR, Generalitat de Catalunya). Additionally, Christian Blum acknowledges support from IKERBASQUE. Evelia Lizárraga acknowledges support from the Mexican National Council for Science and Technology (CONACYT, Doctoral Grant Number 253787).
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Evelia Lizárraga, Maria J. Blesa, Christian Blum, and Günther R. Raidl declare that they have no conflict of interest.
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Lizárraga, E., Blesa, M.J., Blum, C. et al. Large neighborhood search for the most strings with few bad columns problem. Soft Comput 21, 4901–4915 (2017). https://doi.org/10.1007/s00500-016-2379-4
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DOI: https://doi.org/10.1007/s00500-016-2379-4