Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem

Journal of Heuristics

Volumn 14, Issue 5, 2008, Pages 519-547

  Santana, Roberto ^a   Larrañaga, Pedro ^a   Lozano, José A ^a  

^a UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

EDAs; Protein folding; Protein side chain placement; Rotamers; UMDA; VNS

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HEURISTIC PROGRAMMING; LEARNING SYSTEMS; OPTIMIZATION; PHILOSOPHICAL ASPECTS; PROBABILITY;

EDAS; PROTEIN FOLDING; PROTEIN SIDE CHAIN PLACEMENT; ROTAMERS; UMDA; VNS;

LEARNING ALGORITHMS;

EID: 51649105201     PISSN: 13811231     EISSN: 15729397     Source Type: Journal    
DOI: 10.1007/s10732-007-9049-8     Document Type: Article

References (54)

1. Andreatta, A., Ribeiro, C.: Heuristics for the phylogeny problem. J. Heuristics 8, 429-447 (2002)
2. Belacel, N., Hansen, P., Mladenović, N.: Fuzzy J-means: a new heuristic for fuzzy clustering. Pattern Recognit. 35(10), 2193-2200 (2002)
3. Blanco, R., Inza, I., Merino, M., et al.: Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS. J. Biomed. Inform. 38(5), 376-388 (2005)
4. Blickle, T., Thiele, L.: A comparison of selection schemes used in evolutionary algorithms. Evol. Comput. 4(4), 361-394 (1996)
5. Brimberg, J., Hansen, P., Mladenović, N., et al.: Improvements and comparison of heuristics for solving the multisource weber problem. Oper. Res. 48(3), 444-460 (2000)
6. Brimberg, J., Hansen, P., Mladenović, N.: Convergence of variable neighborhood search. Technical Report G-2003-45, Les Cahiers du GERAD (2003)
7. Brimberg, J., Mladenović, N., Urošević, D.: Variable neighborhood search for the k-cardinality subgraph problem. In: Hansen, P., Mladenović, N., Pérez, J.A.M., Batista, B.M., Moreno-Vega, J.M. (eds.) Proceedings of the 18th Mini Euro Conference on Variable Neighborhood Search, 2005
8. Davidović, T., Hansen, P., Mladenović, N.: Permutation-based genetic, tabu and variable neighborhood search heuristics for multiprocessor scheduling with communications delays. Technical Report G-2004-19, Les Cahiers du GERAD (2004)
9. Dawid, A.P.: Applications of a general propagation algorithm for probabilistic expert systems. Stat. Comput. (2), 25-36 (1992)
10. De Maeyer, M., Desmet, J., Lasters, I.: The dead-end elimination theorem: mathematical aspects, implementation, optimization, evaluation, and performance. Methods Mol. Biol. 143, 265-304 (2000)
11. Dunbrack, R.L.: Rotamer libraries in the 21st century. Curr. Opin. Struct. Biol. 12, 431-440 (2002)
12. Dunbrack, R.L., Cohen, F.E.: Bayesian statistical analysis of protein side-chain rotamer preferences. Protein Sci. 6(8), 1661-1681 (1997)
13. Efron, B.: The jackknife, the bootstrap, and other resampling plans. In: CBMS-NSF Regional Conference Series in Applied Mathematics, vol. 38, 1982
14. Etxeberria, R., Larrañaga, P.: Global optimization using Bayesian networks. In: Proceedings of the Second Symposium on Artificial Intelligence CIMAF-99, pp. 151-173, Habana, Cuba, 1999
15. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2-3), 131-163 (1997)
16. García, C.G., Pérez, D., García, F.C.: Parallel variable neighborhood search for the linear ordering problem. In: Hansen, P., Mladenović, N., Pérez, J.A.M., Batista, B.M., Moreno-Vega, J.M. (eds.) Proceedings of the 18th Mini Euro Conference on Variable Neighborhood, 2005
17. Glover, F.: Future paths for Integer programming and links to artificial intelligence. Comput. Oper. Res. 5, 533-549 (1997)
18. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
19. González, C., Lozano, J.A., Larrañaga, P.: Mathematical modeling of discrete estimation of distribution algorithms. In: Larrañaga, P., Lozano, J.A. (eds.) Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation, pp. 143-162. Kluwer Academic, Boston (2002a)
20. González, C., Lozano, J.A., Larrañaga, P.: Mathematical modeling of UMDAc algorithm with tournament selection. Behaviour on linear and quadratic functions. Int. J. Approx. Reason. 31(4), 313-340 (2002b)
21. Hansen, P., Mladenović, N.: Variable neighborhood search: principles and applications. Eur. J. Oper. Res. 130, 449-467 (2001)
22. Hansen, P., Mladenović, N.: Variable neighborhood search. In: Pardalos, P., Resende, M. (eds.) Handbook of Applied Optimization, pp. 221-234. Oxford University Press, London (2002)
23. Hansen, P., Mladenović, N.: Tutorial on variable neighborhood search. Technical Report G-2003-46, Les Cahiers du GERAD (2003a)
24. Hansen, P., Mladenović, N.: Variable neighborhood search. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 145-184. Kluwer Academic, Dordrecht (2003b)
25. Höns, R.: Estimation of distribution algorithms and minimum relative entropy. Ph.D. thesis, University of Bonn, Bonn, Germany (2006)
26. Hsu, J.C.: Multiple Comparisons: Theory and Methods. Chapman & Hall, London (1996)
27. Kochetov, Y., Velikanova, Y.: Variable neighborhood search for the 2D orthogonal packing. In: Hansen, P., Mladenović, N., Pérez, J.A.M., Batista, B.M., Moreno-Vega, J.M. (eds.) Proceedings of the 18th Mini Euro Conference on Variable Neighborhood Search, 2005
28. Kovačević, V., Čangalović, M., Ašić, M., et al.: Tabu search methodology in global optimization. Comput. Math. Appl. 37, 125-133 (1999)
29. Larrañaga, P., Lozano, J.A. (eds.): Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic, Boston (2002)
30. Lauritzen, S., Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their application to expert systems (with discussion). J. R. Stat. Soc. Ser. B 50, 157-224 (1988)
31. Lee, C., Subbiah, S.: Prediction of protein side-chain conformation by packing optimization. J. Mol. Biol. 217, 373-388 (1991)
32. Lozano, J.A., Sagarna, R., Larrañaga, P.: Parallel estimation of distribution algorithms. In: Larrañaga, P., Lozano, J.A. (eds.) Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation, pp. 125-142. Kluwer Academic, Boston (2002)
33. Lozano, J.A., Larrañaga, P., Inza, I., et al.: Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms. Springer, Berlin (2006)
34. Mendiburu, A., Lozano, J., Miguel-Alonso, J.: Parallel implementation of EDAs based on probabilistic graphical models. IEEE Trans. Evol. Comput. 9(4), 406-423 (2005)
35. Mladenović, N.: A variable neighborhood algorithm-a new metaheuristics for combinatorial optimization. In: Abstracts of Papers Presented at Optimization Days, Montréal, p. 112, 1995
36. Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24, 1097-1100 (1997)
37. Mühlenbein, H., Mahnig, T.: Evolutionary synthesis of Bayesian networks for optimization. In: Patel, M., Honavar, V., Balakrishnan, K. (eds.) Advances in Evolutionary Synthesis of Intelligent Agents, pp. 429-455. MIT Press, Cambridge (2001)
38. Mühlenbein, H., Mahnig, T.: Evolutionary optimization and the estimation of search distributions with applications to graph bipartitioning. Int. J. Approx. Reason. 31(3), 157-192 (2002)
39. Mühlenbein, H., Paaß, G.: From recombination of genes to the estimation of distributions I. Binary parameters. In: Voigt, H.-M., Ebeling, W., Rechenberg, I., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature-PPSN IV, LNCS, vol. 1141, pp. 178-187. Springer, Berlin (1996)
40. Nilsson, D.: An efficient algorithm for finding the M most probable configurations in probabilistic expert systems. Stat. Comput. 2, 159-173 (1998)
41. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)
42. Pelikan, M.: Hierarchical Bayesian Optimization Algorithm. Toward a New Generation of Evolutionary Algorithms. Springer, Berlin (2005)
43. Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: BOA: The Bayesian optimization algorithm. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference GECCO-1999, vol. I, pp. 525-532. Morgan Kaufmann, Orlando (1999)
44. Pelikan, M., Goldberg, D.E., Lobo, F.: A survey of optimization by building and using probabilistic models. Comput. Optim. App. 21(1), 5-20 (2002)
45. Peña, J., Lozano, J.A., Larrañaga, P.: Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks. Evol. Comput. 13(1), 43-66 (2005)
46. Robles, V., de Miguel, P., Larrañaga, P.: Solving the traveling salesman problem with EDAs. In: Larrañaga, P., Lozano, J.A. (eds.) Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation, pp. 227-238. Kluwer Academic, Boston (2002)
47. Robles, V., Peña, J.M., Pérez, M.S., et al.: GA-EDA: a new hybrid cooperative search evolutionary algorithm. In: Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E. (eds.) Towards a New Evolutionary Computation. Advances in Estimation of Distribution Algorithms, pp. 187-200. Springer, Berlin (2006)
48. Rodríguez, I., Moreno, J.M., Moreno, J.A.: Variable neighborhood tabu search and its application to the median cycle problem. Eur. J. Oper. Res. 151(2), 365-378 (2003)
49. Voigt, C.A., Gordon, D.B., Mayo, S.L.: Trading accuracy for speed: a quantitative comparison of search algorithms in protein sequence design. J. Mol. Biol. 299(3), 799-803 (2000)
50. Yanover, C., Weiss, Y.: Approximate inference and protein-folding. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems, vol. 15, pp. 1457-1464. MIT Press, Cambridge (2003)
51. Yanover, C., Weiss, Y.: Approximate inference and side-chain prediction (2004a, submitted for publication). Available online from: http://www.leibniz. cs.huji.ac.il/tr/963.pdf
52. Yanover, C., Weiss, Y.: Finding the M most probable configurations using loopy belief propagation. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems, vol. 16, MIT Press, Cambridge (2004b)
53. Zaffalon, M.: The naive credal classifier. J. Stat. Plan. Inference 105, 5-21 (2002)
54. Zhou, A., Zhang, Q., Jin, Y., et al.: A model-based evolutionary algorithm for bi-objective optimization. In: Proceedings of the 2005 Congress on Evolutionary Computation CEC-2005, pp. 2568-2575. IEEE Press, Edinburgh (2005)