Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Machine Learning - Special issue on applications in molecular biology
The Complexity of Some Problems on Subsequences and Supersequences
Journal of the ACM (JACM)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Journal of Global Optimization
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
High Speed Homology Search Using Run-Time Reconfiguration
FPL '02 Proceedings of the Reconfigurable Computing Is Going Mainstream, 12th International Conference on Field-Programmable Logic and Applications
FMGA: Finding Motifs by Genetic Algorithm
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Embedded Computation of Maximum-Likelihood Phylogeny Inference Using Platform FPGA
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
MDGA: motif discovery using a genetic algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Regulatory Motif Discovery Using a Population Clustering Evolutionary Algorithm
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
GPU-MEME: Using Graphics Hardware to Accelerate Motif Finding in DNA Sequences
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
MOGAMOD: Multi-objective genetic algorithm for motif discovery
Expert Systems with Applications: An International Journal
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Bacterial Foraging Optimization Algorithm Integrating Tabu Search for Motif Discovery
BIBM '09 Proceedings of the 2009 IEEE International Conference on Bioinformatics and Biomedicine
Motif Discovery Using Evolutionary Algorithms
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Firefly algorithms for multimodal optimization
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Pattern Recognition Letters
Finding motifs in DNA sequences applying a multiobjective artificial bee colony (MOABC) algorithm
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
An Ultrafast Scalable Many-Core Motif Discovery Algorithm for Multiple GPUs
IPDPSW '11 Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
Accelerating motif discovery: motif matching on parallel hardware
WABI'06 Proceedings of the 6th international conference on Algorithms in Bioinformatics
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A Parallel Multi-Core Team of Multiobjective Evolutionary Algorithms to Discover DNA Motifs
HPCC '12 Proceedings of the 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems
Comparing multiobjective swarm intelligence metaheuristics for DNA motif discovery
Engineering Applications of Artificial Intelligence
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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.