Hybrid multiobjective artificial bee colony with differential evolution applied to motif finding

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7833 LNCS, Issue , 2013, Pages 68-79

  González Álvarez, David L ^a   Vega Rodríguez, Miguel A ^a  

^a UNIVERSITY OF EXTREMADURA   (Spain)

Author keywords

Artificial bee colony; differential evolution; DNA; motif discovery; multiobjective optimization

Indexed keywords

ALGORITHM FOR SOLVING; ARTIFICIAL BEE COLONIES; BIOLOGICAL SIGNIFICANCE; DIFFERENTIAL EVOLUTION; DROSOPHILA MELANOGASTER; MOTIF DISCOVERY; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; MULTI-OBJECTIVE ARTIFICIAL BEE COLONIES;

BINDING SITES; BIOINFORMATICS; DATA MINING; DNA; LEARNING SYSTEMS; MULTIOBJECTIVE OPTIMIZATION; PROBLEM SOLVING; YEAST;

EVOLUTIONARY ALGORITHMS;

EID: 84875115078     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-37189-9_7     Document Type: Conference Paper

References (23)

1. Che, D., Song, Y., Rashedd, K.: MDGA: Motif discovery using a genetic algorithm. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation (GECCO 2005), pp. 447-452 (2005)
2. Congdon, C.B., Fizer, C.W., Smith, N.W., Gaskins, H.R., Aman, J., Nava, G.M., Mattingly, C.: Preliminary results for GAMI: A genetic algorithms approach to motif inference. In: Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2005), pp. 97-104 (2005)
3. Deb, K.: Multi-objective optimization using evolutionary algorithms. John Wiley & Sons (2001)
4. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182-197 (2002)
5. D'haeseleer, P.: What are DNA sequence motifs? Nature Biotechnology 24(4), 423-425 (2006)
6. Fogel, G.B., et al.: Evolutionary computation for discovery of composite transcription factor binding sites. Nucleic Acids Research 36(21), 1-14 (2008)
7. Fogel, G.B., Weekes, D.G., Varga, G., Dow, E.R., Harlow, H.B., Onyia, J.E., Su, C.: Discovery of sequencemotifs related to coexpression of genes using evolutionary computation. Nucleic Acids Research 32(13), 3826-3835 (2004)
8. González-Álvarez, D.L., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Predicting DNA motifs by using evolutionary multiobjective optimization. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 42(6), 913-925 (2011)
9. González- Álvarez, D.L., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Comparing multiobjective swarm intelligence metaheuristics for DNA motif discovery. Engineering Applications of Artificial Intelligence 26(1), 314-326 (2012)
10. Grosan, C., Abraham, A.: Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews. In: Grosan, C., Abraham, A., Ishibuchi, H. (eds.) Hybrid Evolutionary Algorithms. SCI, vol. 75, pp. 1-17. Springer, Heidelberg (2007)
11. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Turkey (2005)
12. Kaya, M.: MOGAMOD: Multi-objective genetic algorithm for motif discovery. Expert Systems with Applications 36(2), 1039-1047 (2009)
13. Liu, F.F.M., Tsai, J.J.P., Chen, R.M., Chen, S.N., Shih, S.H.: FMGA: finding motifs by genetic algorithm. In: Fourth IEEE Symposium on Bioinformatics and Bioengineering (BIBE 2004), pp. 459-466 (2004)
14. Lones, M.A., Tyrrell, A.M.: Regulatory motif discovery using a population clustering evolutionary algorithm. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4(3), 403-414 (2007)
15. Paul, T.K., Iba, H.: Identification of weak motifs in multiple biological sequences using genetic algorithm. In: Proceedings of the 2006 Conference on Genetic and Evolutionary Computation (GECCO 2006), pp. 271-278 (2006)
16. Rubio-Largo, A., González-Álvarez, D.L., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez- Pérez, J.M.: MO-ABC/DE - multiobjective artificial bee colony with differential evolution for unconstrained multiobjective optimization. In: 13th IEEE International Symposium on Computational Intelligence and Informatics, pp. 157-162 (2012)
17. Shao, L., Chen, Y.: Bacterial foraging optimization algorithm integrating tabu search for motif discovery. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2009), pp. 415-418 (2009)
18. Stine, M., Dasgupta, D., Mukatira, S.: Motif discovery in upstream sequences of coordinately expressed genes. In: The 2003 Congress on Evolutionary Computation (CEC 2003), vol. 3, pp. 1596-1603 (2003)
19. Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341-359 (1997)
20. Tompa, M., et al.: Assessing computational tools for the discovery of transcription factor binding sites. Nature Biotechnology 23(1), 137-144 (2005)
21. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computation 8(2), 173-195 (2000)
22. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. Technical report tik-report 103, Swiss Federal Institute of Technology, Zurich, Switzerland (2001)
23. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257-271 (1999)