A probabilistic beam search approach to the shortest common supersequence problem
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...
| Autores: | , , , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2006 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/87415 |
| Acceso en línea: | https://hdl.handle.net/2117/87415 |
| Access Level: | acceso abierto |
| Palabra clave: | Combinatorial mathematics Greedy algorithms Optimisation Probability Search problems String matching Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica |
| Sumario: | 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. |
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