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Journal of Heuristics

, Volume 13, Issue 3, pp 209–225 | Cite as

Sequencing by hybridization: an enhanced crossover operator for a hybrid genetic algorithm

  • Carlos A. Brizuela
  • Luis C. González-Gurrola
  • Andrei Tchernykh
  • Denis Trystram
Article

Abstract

This paper presents a genetic algorithm for an important computational biology problem. The problem appears in the computational part of a new proposal for DNA sequencing denominated sequencing by hybridization. The general usage of this method for real sequencing purposes depends mainly on the development of good algorithmic procedures for solving its computational phase. The proposed genetic algorithm is a modified version of a previously proposed hybrid genetic algorithm for the same problem. It is compared with two well suited meta-heuristic approaches reported in the literature: the hybrid genetic algorithm, which is the origin of our proposed variant, and a tabu-scatter search algorithm. Experimental results carried out on real DNA data show the advantages of using the proposed algorithm. Furthermore, statistical tests confirm the superiority of the proposed variant over the state-of-the-art heuristics.

Keywords

Sequencing by hybridization Hybrid genetic algorithm Greedy crossover 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Carlos A. Brizuela
    • 1
  • Luis C. González-Gurrola
    • 2
  • Andrei Tchernykh
    • 1
  • Denis Trystram
    • 3
  1. 1.Computer Sciences DepartmentCICESE Research CenterEnsenadaMexico
  2. 2.Instituto Tecnológico Superior de Santiago PapasquiaroSantiago PapasquiaroMexico
  3. 3.ID-Institut IMAGGrenoble Cedex 9France

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