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

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Abstract

The aim of this work is to introduce several proposals for combining two metaheuristics: variable neighborhood search (VNS) and estimation of distribution algorithms (EDAs). Although each of these metaheuristics has been previously hybridized in several ways, this paper constitutes the first attempt to combine both optimization methods.

The different ways of combining VNS and EDAs will be classified into three groups. In the first group, we will consider combinations where the philosophy underlying VNS is embedded in EDAs. Considering different neighborhood spaces (points, populations or probability distributions), we will obtain instantiations for the approaches in this group. The second group of algorithms is obtained when probabilistic models (or any other machine learning paradigm) are used in order to exploit the good and bad shakes of the randomly generated solutions in a reduced variable neighborhood search. The last group of algorithms contains the results of alternating VNS and EDAs.

An application of the first approach is presented in the protein side chain placement problem. The results obtained show the superiority of the hybrid algorithm in comparison with EDAs and VNS.

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References

  • Andreatta, A., Ribeiro, C.: Heuristics for the phylogeny problem. J. Heuristics 8, 429–447 (2002)

    Article  MATH  Google Scholar 

  • Belacel, N., Hansen, P., Mladenović, N.: Fuzzy J-means: a new heuristic for fuzzy clustering. Pattern Recognit. 35(10), 2193–2200 (2002)

    Article  MATH  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Blickle, T., Thiele, L.: A comparison of selection schemes used in evolutionary algorithms. Evol. Comput. 4(4), 361–394 (1996)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Brimberg, J., Hansen, P., Mladenović, N.: Convergence of variable neighborhood search. Technical Report G–2003–45, Les Cahiers du GERAD (2003)

  • 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

  • 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)

  • Dawid, A.P.: Applications of a general propagation algorithm for probabilistic expert systems. Stat. Comput. (2), 25–36 (1992)

  • 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)

    Google Scholar 

  • Dunbrack, R.L.: Rotamer libraries in the 21st century. Curr. Opin. Struct. Biol. 12, 431–440 (2002)

    Article  Google Scholar 

  • Dunbrack, R.L., Cohen, F.E.: Bayesian statistical analysis of protein side-chain rotamer preferences. Protein Sci. 6(8), 1661–1681 (1997)

    Article  Google Scholar 

  • Efron, B.: The jackknife, the bootstrap, and other resampling plans. In: CBMS-NSF Regional Conference Series in Applied Mathematics, vol. 38, 1982

  • 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

  • Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)

    Article  MATH  Google Scholar 

  • 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

  • Glover, F.: Future paths for Integer programming and links to artificial intelligence. Comput. Oper. Res. 5, 533–549 (1997)

    MathSciNet  Google Scholar 

  • Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison–Wesley, Reading (1989)

    MATH  Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Article  MATH  Google Scholar 

  • Hansen, P., Mladenović, N.: Variable neighborhood search: principles and applications. Eur. J. Oper. Res. 130, 449–467 (2001)

    Article  MATH  Google Scholar 

  • 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)

    Google Scholar 

  • Hansen, P., Mladenović, N.: Tutorial on variable neighborhood search. Technical Report G–2003–46, Les Cahiers du GERAD (2003a)

  • Hansen, P., Mladenović, N.: Variable neighborhood search. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 145–184. Kluwer Academic, Dordrecht (2003b)

    Chapter  Google Scholar 

  • Höns, R.: Estimation of distribution algorithms and minimum relative entropy. Ph.D. thesis, University of Bonn, Bonn, Germany (2006)

  • Hsu, J.C.: Multiple Comparisons: Theory and Methods. Chapman & Hall, London (1996)

    MATH  Google Scholar 

  • 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

  • Kovačević, V., Čangalović, M., Ašić, M., et al.: Tabu search methodology in global optimization. Comput. Math. Appl. 37, 125–133 (1999)

    MATH  Google Scholar 

  • Larrañaga, P., Lozano, J.A. (eds.): Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic, Boston (2002)

    MATH  Google Scholar 

  • 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)

    MathSciNet  MATH  Google Scholar 

  • Lee, C., Subbiah, S.: Prediction of protein side-chain conformation by packing optimization. J. Mol. Biol. 217, 373–388 (1991)

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • Lozano, J.A., Larrañaga, P., Inza, I., et al.: Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms. Springer, Berlin (2006)

    MATH  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Mladenović, N.: A variable neighborhood algorithm—a new metaheuristics for combinatorial optimization. In: Abstracts of Papers Presented at Optimization Days, Montréal, p. 112, 1995

  • Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24, 1097–1100 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Article  MATH  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Nilsson, D.: An efficient algorithm for finding the M most probable configurations in probabilistic expert systems. Stat. Comput. 2, 159–173 (1998)

    Article  Google Scholar 

  • Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)

    Google Scholar 

  • Pelikan, M.: Hierarchical Bayesian Optimization Algorithm. Toward a New Generation of Evolutionary Algorithms. Springer, Berlin (2005)

    MATH  Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Article  MathSciNet  MATH  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Article  MATH  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • 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

  • 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)

    Google Scholar 

  • Zaffalon, M.: The naive credal classifier. J. Stat. Plan. Inference 105, 5–21 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  • 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)

    Google Scholar 

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  1. Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizábal 1, 20080, San Sebastián–Donostia, Spain

    Roberto Santana, Pedro Larrañaga & José A. Lozano

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  1. Roberto Santana
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Correspondence to Roberto Santana.

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Santana, R., Larrañaga, P. & Lozano, J.A. Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem. J Heuristics 14, 519–547 (2008). https://doi.org/10.1007/s10732-007-9049-8

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