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A Probabilistic Beam Search Approach to the Shortest Common Supersequence Problem

  • Christian Blum
  • Carlos Cotta
  • Antonio J. Fernández
  • José E. Gallardo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4446)

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.

Keywords

Problem Instance Partial Solution Probabilistic Beam Vertex Cover Memetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Christian Blum
    • 1
  • Carlos Cotta
    • 2
  • Antonio J. Fernández
    • 2
  • José E. Gallardo
    • 2
  1. 1.ALBCOM, Dept. Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, BarcelonaSpain
  2. 2.Dept. Lenguajes y Ciencias de la Computación, ETSI Informática, Universidad de Málaga, MálagaSpain

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