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%.
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References
D’haeseleer, P.: What are DNA sequence motifs? Nature Biotechnology 24(4), 423–425 (2006)
Fogel, G.B., et al.: Evolutionary computation for discovery of composite transcription factor binding sites. Nucleic Acids Reseach 36(21), e142 (2008)
Price, K., Storn, R.: Differential Evolution - A Simple Evolution Strategy for Fast Optimization. Dr. Dobb’s Journal 22(4), 18–24, 78 (1997)
González-Álvarez, D.L., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Solving the Motif Discovery Problem by Using Differential Evolution with Pareto Tournaments. In: CEC 2010, pp. 4140–4147. IEEE Computer Society, Barcelona (2010)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. IEEE Transactions on Evolutionary Computation 8(2), 173–195 (2000)
Wingender, E., Dietze, P., Karas, H., Knüppel, R.: TRANSFAC: a database on transcription factors and their DNA binding sites. Nucleic Acids Research 24(1), 238–241 (1996)
Tompa, M., et al.: Assessing computational tools for the discovery of transcription factor binding sites. Nature Biotechnology 23(1), 137–144 (2005)
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González-Álvarez, D.L., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M. (2012). Discovering DNA Motifs with a Parallel Shared Memory Differential Evolution. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_30
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DOI: https://doi.org/10.1007/978-3-642-27549-4_30
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