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A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery

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Abstract

When solving a wide range of complex scenarios of a given optimization problem, it is very difficult, if not impossible, to develop a single technique or algorithm that is able to solve all of them adequately. In this case, it is necessary to combine several algorithms by applying the most appropriate one in each case. Parallel computing can be used to improve the quality of the solutions obtained in a cooperative algorithms model. Exchanging information between parallel cooperative algorithms will alter their behavior in terms of solution searching, and it may be more effective than a sequential metaheuristic. For demonstrating this, a parallel cooperative team of four multiobjective evolutionary algorithms based on OpenMP is proposed for solving different scenarios of the Motif Discovery Problem (MDP), which is an important real-world problem in the biological domain. As we will see, the results show that the application of a properly configured parallel cooperative team achieves high quality solutions when solving the addressed problem, improving those achieved by the algorithms executed independently for a much longer time.

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Acknowledgements

This work was partially funded by the Spanish Ministry of Economy and Competitiveness and the ERDF (European Regional Development Fund), under the Contract TIN2012-30685 (BIO Project). Thanks also extend to the Fundación Valhondo for the economic support offered to David L. González-Álvarez.

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  1. Department Technologies of Computers and Communications, ARCO Research Group, University of Extremadura, Escuela Politécnica, Campus Universitario s/n, 10003, Cáceres, Spain

    David L. González-Álvarez & Miguel A. Vega-Rodríguez

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  1. David L. González-Álvarez
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  2. Miguel A. Vega-Rodríguez
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Correspondence to David L. González-Álvarez.

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González-Álvarez, D.L., Vega-Rodríguez, M.A. A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery. J Supercomput 66, 1576–1612 (2013). https://doi.org/10.1007/s11227-013-0951-6

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