Obtaining Biclusters in Microarrays with Population-Based Heuristics

  • Pablo Palacios
  • David Pelta
  • Armando Blanco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


In this article, we shall analyze the behavior of population-based heuristics for obtaining biclusters from DNA microarray data. More specifically, we shall propose an evolutionary algorithm, an estimation of distribution algorithm, and several memetic algorithms that differ in the local search used.

In order to analyze the effectiveness of the proposed algorithms, the freely available yeast microarray dataset has been used. The results obtained have been compared with the algorithm proposed by Cheng and Church.

Both in terms of the computation time and the quality of the solutions, the comparison reveals that a standard evolutionary algorithm and the estimation of distribution algorithm offer an efficient alternative for obtaining biclusters.


Local Search Taboo Search Memetic Algorithm Distribution Algorithm Local Search Scheme 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pablo Palacios
    • 1
  • David Pelta
    • 2
  • Armando Blanco
    • 2
  1. 1.Dept. de Ingeniería Electrónica, Sistemas Informáticos y AutomáticaUniversidad de HuelvaHuelva
  2. 2.Depto. de Ciencias de la Computación e I.A.Universidad de GranadaGranadaSpain

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