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Finding Motifs in DNA Sequences Applying a Multiobjective Artificial Bee Colony (MOABC) Algorithm., , , and . EvoBio, volume 6623 of Lecture Notes in Computer Science, page 89-100. Springer, (2011)Using a Parallel Team of Multiobjective Evolutionary Algorithms to Solve the Motif Discovery Problem., , , and . DCAI, volume 79 of Advances in Intelligent and Soft Computing, page 569-576. Springer, (2010)Parallel H4MSA for Multiple Sequence Alignment., , and . TrustCom/BigDataSE/ISPA (3), page 242-247. IEEE, (2015)978-1-4673-7952-6.Parallelizing a hybrid multiobjective differential evolution for identifying cis-regulatory elements., and . EuroMPI, page 223-228. ACM, (2013)Solving the reporting cells problem by using a parallel team of evolutionary algorithms., , , , , and . Log. J. IGPL, 20 (4): 722-731 (2012)A Parallel Cooperative Evolutionary Strategy for Solving the Reporting Cells Problem., , , , , and . SOCO, volume 73 of Advances in Intelligent and Soft Computing, page 71-78. Springer, (2010)Searching for common patterns on protein sequences by means of a parallel hybrid honey-bee mating optimization algorithm., , and . Parallel Comput., (2018)A Multiobjective SFLA-Based Technique for Predicting Motifs in DNA Sequences., and . EUROCAST (1), volume 8111 of Lecture Notes in Computer Science, page 235-242. Springer, (2013)Solving the motif discovery problem by using Differential Evolution with Pareto Tournaments., , , and . IEEE Congress on Evolutionary Computation, page 1-8. IEEE, (2010)On the scalability of multi-objective metaheuristics for the software scheduling problem., , , and . ISDA, page 1110-1115. IEEE, (2011)