Ant-CSP: An ant colony optimization algorithm for the closest string problem

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

Volumn 5901 LNCS, Issue , 2010, Pages 370-381

  Faro, Simone ^a   Pappalardo, Elisa ^a  

^a UNIVERSITY OF CATANIA   (Italy)

Author keywords

Ant colony optimization; Closest string problem; Metaheuristic algorithms; NP hard problems; String comparison problems

Indexed keywords

ANT-COLONY OPTIMIZATION; CLOSEST STRING PROBLEM; META HEURISTIC ALGORITHM; NP-HARD PROBLEM; STRING COMPARISON;

BIOCHEMISTRY; COMPUTATIONAL COMPLEXITY; COMPUTER OPERATING PROCEDURES; COMPUTER PROGRAMMING; HAMMING DISTANCE; HEURISTIC METHODS; INFORMATION THEORY; MOLECULAR BIOLOGY; PACKET NETWORKS; SET THEORY; SIMULATED ANNEALING;

ALGORITHMS;

EID: 77249099325     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-11266-9_31     Document Type: Conference Paper

References (25)

1. Booker, L.B., Goldberg, D.E., Holland, J.H.: Classifier Systems and Genetic Algorithms. Artif. Intell. 40(1-3), 235-282 (1989)
2. Deneubourg, J.L., Aron, S., Goss, S., Pasteels, J.M.: The Self-Organizing Exploratory Pattern of the Argentine Ant. Journal of Insect Behavior 3(2), 159-168 (1990)
3. Dorigo, M.: Optimization, Learning and Natural Algorithms. PhD Thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy (1992)
4. Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant Algorithms for Discrete Optimization. Artificial Life 5(2), 137-172 (1999)
5. Frances, M., Litman, A.: On Covering Problems of Codes. Theory of Computing Systems 30(2), 113-119 (1997)
6. Gasieniec, L., Jansson, J., Lingas, A.: Efficient Approximation Algorithms for the Hamming Center Problem. In: Proceedings of the 10th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 905-906. Society for Industrial and Applied Mathematics, Philadelphia (1999)
7. Goldberg, D.E., Holland, J.H.: Genetic Algorithms and Machine Learning. Machine Learning 3(2), 95-99 (1988)
8. Gomes, F.C., Meneses, C.N., Pardalos, P.M., Viana, G.V.R.: A Parallel Multistart Algorithm for the Closest String Problem. Computers and Operations Research 35(11), 3636-3643 (2008)
9. Gramm, J., Niedermeier, R., Rossmanith, P.: Exact Solutions for Closest String and Related Problems. In: Eades, P., Takaoka, T. (eds.) ISAAC 2001. LNCS, vol. 2223, pp. 441-453. Springer, Heidelberg (2001)
10. Gramm, J., Niedermeier, R., Rossmanith, P.: Fixed-Parameter Algorithms for Closest String and Related Problems. Algorithmica 37(1), 25-42 (2003)
11. Hertz, G.Z., Hartzell, G.W., Stormo, G.D.: Identification of Consensus Patterns in Unaligned DNA Sequences Known to Be Functionally Related. Bioinformatics 6(2), 81-92 (1990)
12. Hochba, D.S.: Approximation algorithms for NP-hard problems, vol. 28. ACM, New York (1997)
13. Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1992)
14. Holland, J.H.: Genetic Algorithms Computer Programs that "Evolve" in Ways that Resemble Natural Selection Can Solve Complex Problems even Their Creators Do not Fully Understand. Scientific American 267, 62-72 (1992)
15. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671-680 (1983)
16. Lanctot, J.K., Li, M., Ma, B., Wang, S., Zhang, L.: Distinguishing String Selection Problems. In: Proceedings of the 10th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 633-642. Society for Industrial and Applied Mathematics, Philadelphia (1999)
17. Li, M., Ma, B., Wang, L.: On the Closest String and Substring Problems. Journal of the ACM 49(2), 157-171 (2002)
18. Liu, X., He, H., Sykora, O.: Parallel Genetic Algorithm and Parallel Simulated Annealing Algorithm for the Closest String Problem. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS (LNAI), vol. 3584, pp. 591-597. Springer, Heidelberg (2005)
19. Liu, X., Holger, M., Hao, Z., Wu, G.: A Compounded Genetic and Simulated Annealing Algorithm for the Closest String Problem. In: 2nd International Conference on Bioinformatics and Biomedical Engineering, ICBBE 2008, pp. 702-705 (2008)
20. Ma, B., Sun, X.: More Efficient Algorithms for Closest String and Substring Problems. In: Vingron, M., Wong, L. (eds.) RECOMB 2008. LNCS (LNBI), vol. 4955, pp. 396-409. Springer, Heidelberg (2008)
21. Meneses, C.N., Lu, Z., Oliveira, C.A.S., Pardalos, P.M.: Optimal Solutions for the Closest-String Problem via Integer Programming. Informs Journal on Computing 16(4), 419-429 (2004)
22. Metropolis, N., Rosenbluth, A.E., Rosenbluth, M.N., Teller, A.H., Teller, E.: Perspective on Equation of State Calculations by Fast Computing Machines. J. Chem. Phys. 21, 1087-1092 (1953)
23. Metropolis, N., Ulam, S.: The Monte Carlo Method. Journal of the American Statistical Association 44(247), 335-341 (1949)
24. Roman, S.: Coding and information theory. Springer, Heidelberg (1992)
25. Stormo, G.D., Hartzell, G.W.: Identifying Protein-Binding Sites from Unaligned DNA Fragments. Proc. Natl. Acad. Sci. USA 86, 1183-1187 (1989)