Abstract
The Shortest Common Supersequence Problem (SCSP) is a well-known hard combinatorial optimization problem that formalizes many real world problems. This paper presents a novel randomized search strategy, called probabilistic beam search (PBS), based on the hybridization between beam search and greedy constructive heuristics. PBS is competitive (and sometimes better than) previous state-of-the-art algorithms for solving the SCSP. The paper describes PBS and provides an experimental analysis (including comparisons with previous approaches) that demonstrate its usefulness.
This work was supported by grants TIN2004-7943-C04-01 and TIN2005-08818-C04-01 of the Spanish government. Christian Blum acknowledges support from the Ramón y Cajal program of the Spanish Ministry of Science and Technology.
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Blum, C., Cotta, C., Fernández, A.J., Gallardo, J.E. (2007). A Probabilistic Beam Search Approach to the Shortest Common Supersequence Problem. In: Cotta, C., van Hemert, J. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2007. Lecture Notes in Computer Science, vol 4446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71615-0_4
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