Discovering DNA motifs with a parallel shared memory differential evolution

  • Authors:

  • David L. Gonz$#225/lez-Á/lvarez;Miguel A. Vega-Rodrí/guez;Juan A. Gó/mez-Pulido;Juan M. S$#225/nchez-Pé/rez

  • Affiliations:

  • Department of Technologies of Computers and Communications, ARCO Research Group, University of Extremadura, C$#225/ceres, Spain;Department of Technologies of Computers and Communications, ARCO Research Group, University of Extremadura, C$#225/ceres, Spain;Department of Technologies of Computers and Communications, ARCO Research Group, University of Extremadura, C$#225/ceres, Spain;Department of Technologies of Computers and Communications, ARCO Research Group, University of Extremadura, C$#225/ceres, Spain

  • Venue:
  • EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
  • Year:
  • 2011

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Abstract

The usefulness and efficiency of one algorithm to solve an optimization problem is not given only by the quality of the results obtained, it is also important the computational time and the resources required to obtain them. In this paper we present a parallel implementation of the Differential Evolution (DE) to solve the Motif Discovery Problem (MDP). MDP is an important biological problem that can have a high computational cost if we work with large amounts of nucleotides, so the fine-grained parallelism on a shared memory machine can help us to achieve results quickly. To ensure that our heuristic obtains relevant results we have compared them with those obtained by the standard algorithm NSGA-II and with other fourteen well-known biological methods. As we will see, the structure of the algorithm makes it well suited for parallelization, achieving good results and efficiencies up to 95%.