Abstract
The longest common subsequence problem is a classical string problem. It has applications, for example, in pattern recognition and bioinformatics. This contribution proposes an integrative hybrid metaheuristic for this problem. More specifically, we propose a variable neighborhood search that applies an iterated greedy algorithm in the improvement phase and generates the starting solutions by invoking either beam search or a greedy randomized procedure. The main motivation of this work is the lack of fast neighborhood search methods for the tackled problem. The benefits of the proposal in comparison to the state of the art are experimentally shown.
This work was supported by the Research Projects TIN2008-05854, P08-TIC-4173, and by grant TIN2007-66523 (FORMALISM) of the Spanish government. In addition, Christian Blum acknowledges support from the Ramón y Cajal program of the Spanish government of which he is a research fellow.
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Lozano, M., Blum, C. (2010). A Hybrid Metaheuristic for the Longest Common Subsequence Problem. In: Blesa, M.J., Blum, C., Raidl, G., Roli, A., Sampels, M. (eds) Hybrid Metaheuristics. HM 2010. Lecture Notes in Computer Science, vol 6373. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16054-7_1
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