A comprehensive comparison of metaheuristics for the repetition-free longest common subsequence problem
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This paper deals with an NP-hard string problem from the bio-informatics field: the repetition-free longest common subsequence problem. This problem has enjoyed an increasing interest in recent years, which has resulted in the application of several pure as well as hybrid metaheuristics. However, the literature lacks a comprehensive comparison between those approaches. Moreover, it has been shown that general purpose integer linear programming solvers are very efficient for solving many of the problem instances that were used so far in the literature. Therefore, in this work we extend the available benchmark set, adding larger instances to which integer linear programming solvers cannot be applied anymore. Moreover, we provide a comprehensive comparison of the approaches found in the literature. Based on the results we propose a hybrid between two of the best methods which turns out to inherit the complementary strengths of both methods.
KeywordsRepetition-free longest common subsequence Hybrid metaheuristics Matheuristic
This work was supported by Project TIN2012-37930-c02-02 (Spanish Ministry for Economy and Competitiveness, feder funds from the European Union). Maria J. Blesa acknowledges support by funds from the agaur of the Government of Catalonia under Project Ref. SGR 2014:1034 (albcom). Our experiments have been executed in the High Performance Computing environment managed by the rdlab at the Technical University of Barcelona (http://rdlab.cs.upc.edu) and we would like to thank them for their support.
- Adi, S.S., Braga, M.D.V., Fernandes, C.G., Ferreira, C.E., Martinez, F.V., Sagot, M.F., Stefanes, M.A., Tjandraatmadja, C., Wakabayashi, Y.: Repetition-free longest common subsequence. Electron. In: Proceedings of The IV Latin-American Algorithms, Graphs, and Optimization Symposium. Notes Discret. Math. 30, 243–248 (2008)Google Scholar
- Blum, C., Blesa, M.J.: Construct, merge, solve and adapt: application to the repetition-free longest common subsequence problem. In: Chicano, F., Hu, B. (eds.) Proceedings of EvoCOP 2016—16th European Conference on Evolutionary Computation in Combinatorial Optimization. Lecture Notes in Computer Science, vol. 9595, pp. 46–57. Springer, Berlin (2016)Google Scholar
- Blum, C., Blesa, M.J., Calvo, B.: Beam-ACO for the repetition-free longest common subsequence problem. In: Legrand, P., Corsini, M.M., Hao, J.K., Monmarché, N., Lutton, E., Schoenauer, M. (eds.) Proceedings of EA 2013—11th Conference on Artificial Evolution, Lecture Notes in Computer Science, vol. 8752, pp. 79–90. Springer, Berlin (2014)Google Scholar
- López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., Birattari, M.: The irace package, iterated race for automatic algorithm configuration. Technical report TR/IRIDIA/2011-004, IRIDIA, Université libre de Bruxelles, Belgium (2011)Google Scholar
- Storer, J.: Data Compression: Methods and Theory. Computer Science Press, Rockville, MD (1988)Google Scholar
- Wang, Q., Pan, M., Shang, Y., Korkin, D.: A fast heuristic search algorithm for finding the longest common subsequence of multiple strings. In: Proceedings of AAAI—Conference on Artificial Intelligence, pp. 1287–1292 (2010)Google Scholar