Performance standards and evaluations in IR test collections: cluster-based retrieval models
Information Processing and Management: an International Journal
FMGA: Finding Motifs by Genetic Algorithm
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Improving particle swarm optimization with differentially perturbed velocity
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
A perturbed particle swarm algorithm for numerical optimization
Applied Soft Computing
Identification of transcription factor binding sites using hybrid particle swarm optimization
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
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Identification of transcription factor binding sites is a vital task in contemporary biology, since it helps researchers to comprehend the regulatory mechanism of gene expression. Computational tools to perform this task have gained great attention since they are good alternatives to expensive and laborious biological experiments. In this paper, we propose a Particle Swarm Optimization based motif-finding method that utilizes a proven Bayesian Scoring Scheme as the fitness function. Since PSO is designed to work in multidimensional continuous domains, this paper presents required developments to adapt PSO for the motif finding application. Furthermore, this paper presents a benchmark of PSO variants with four separate population topologies, GBest, Bidirectional Ring, Random and Von Neumann. Simulations performed over synthetic and real data sets have shown that the proposed method is efficient and also superior to some well-known existing tools. Additionally, the Bidirectional Ring topology appears to be remarkable for the motif-finding application.