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
Comparing multiobjective artificial bee colony adaptations for discovering DNA motifs
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Comparing multiobjective swarm intelligence metaheuristics for DNA motif discovery
Engineering Applications of Artificial Intelligence
Hybrid multiobjective artificial bee colony with differential evolution applied to motif finding
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Designing a novel hybrid swarm based multiobjective evolutionary algorithm for finding DNA motifs
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Journal of Global Optimization
A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery
The Journal of Supercomputing
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Extracting motifs in the sea of DNA sequences is an intricate task but have great significance. We propose an alternative solution integrating bacterial foraging optimization (BFO) algorithm and Tabu Search (TS) algorithm namely TS-BFO. We modify the original BFO via established a self-control multi-length chemotactic step mechanism, and introduce Rao metric. The experiments on real data set extracted from TRANSFAC and SCPD database have predicted meaningful motif which demonstrated that TS BFO is a promising approach for motif discovery.