DocumentCode :
1825657
Title :
A Parallel Multi-Core Team of Multiobjective Evolutionary Algorithms to Discover DNA Motifs
Author :
Gonzalez-Alvarez, David L. ; Vega-Rodríguez, Miguel A. ; Gómez-Pulido, Juan A. ; Sanchez-Perez, Juan M.
Author_Institution :
Dept. Technol. of Comput. & Commun. Escuela Politec., Univ. of Extremadura, Cáceres, Spain
fYear :
2012
fDate :
25-27 June 2012
Firstpage :
17
Lastpage :
24
Abstract :
This paper proposes the use of a parallel multi-core team to exploit the characteristics of several algorithms, when solving one of the best known biological problems, the Motif Discovery Problem (MDP). Often, when we solve an optimization problem, we have to tackle very different scenarios. This complicates finding a single technique or algorithm that will be able to solve all of them adequately. One possible solution to this problem is to combine different algorithms in a team that allows us to solve all scenarios. A parallel methodology is designed in this work, which combines the operation of four multiobjective evolutionary algorithms by using OpenMP to solve different motif discovery scenarios. Our conclusions show that our parallel multi-core team is quite flexible and accurate for this application, and an important tool for discovering relevant DNA motifs.
Keywords :
DNA; biology computing; evolutionary computation; multiprocessing systems; open systems; optimisation; parallel processing; DNA motif discovery problem; MDP; OpenMP; biological problems; multiobjective evolutionary algorithms; optimization problem; parallel methodology; parallel multicore team; DNA; Evolutionary computation; Heuristic algorithms; Optimization; Sociology; Statistics; Multi-core; OpenMP; evolutionary algorithms; motif discovery; multiobjective optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 IEEE 14th International Conference on
Conference_Location :
Liverpool
Print_ISBN :
978-1-4673-2164-8
Type :
conf
DOI :
10.1109/HPCC.2012.13
Filename :
6332154
Link To Document :
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