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An Analysis of Dynamic Mutation Operators for Conformational Sampling

  • Alexandru-Adrian Tantar
  • Nouredine Melab
  • El-Ghazali Talbi
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 210)

Abstract

A comparison analysis of dynamic mutation operators is proposed, having the conformational sampling problem as a case study. The analysis is sustained by a parallel Optimal Computing Budget Allocation (OCBA) selection procedure, employed in order to attain computational speedup. A Pearson system distribution based mutation operator is proposed, allowing for a highly flexible construction. As defined by a set of four parameters, the mean, variance, skewness and kurtosis, a large number of distributions can be simulated. As determined by the analysis outcomes, the class of operators exhibiting significant energy minimization or Root Mean Square Deviation (RMSD) bias is identified. Experiments are carried out on a large number of computational resources, allowing for the outline of an automatic a priori operator tuning and selection methodology. Although not presented in this chapter, similar complementary studies have been conducted on intensification operators and local search algorithms.

Keywords

Root Mean Square Deviation Mutation Operator Local Search Algorithm Conformational Sampling Annealing Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Trans. Evolutionary Computation 6(5), 443–462 (2002)CrossRefGoogle Scholar
  2. 2.
    Alba, E., GLuque, E.G.T., Melab, N.: Metaheuristics and parallelism. In: Alba, E. (ed.) Parallel Metaheuristics. Wiley Series on Parallel and Distributed Computing. Wiley, Chichester (2005)CrossRefGoogle Scholar
  3. 3.
    Anfinsen, C.B., Haber, E., Sela, M., White, F.H.: The Kinetics of Formation of Native Ribonuclease During Oxidation of the Reduced Polypeptide Chain. Proceedings of the National Academy of Sciences of the United States of America 47(9), 1309–1314 (1961)CrossRefGoogle Scholar
  4. 4.
    Blundell, T.L., Sibanda, B.L., Sternberg, M.J., Thornton, J.M.: Knowledge-based prediction of protein structures and the design of novel molecules. Nature 326(6111), 347–352 (1987)CrossRefGoogle Scholar
  5. 5.
    Branke, J., Chick, S.E., Schmidt, C.: New developments in ranking and selection: an empirical comparison of the three main approaches. In: WSC 2005: Proceedings of the 37th Conference on Winter Simulation, pp. 708–717. ACM, New York (2005)Google Scholar
  6. 6.
    Branke, J., Chick, S.E., Schmidt, C.: Selecting a selection procedure. Management Science 53(12), 1916–1932 (2007)CrossRefGoogle Scholar
  7. 7.
    Byrd, R.H., Lu, P., Nocedal, J., Zhu, C.Y.: A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing 16(6), 1190–1208 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Cahon, S., Melab, N., Talbi, E.G.: Paradiseo: A framework for the reusable design of parallel and distributed metaheuristics. Journal of Heuristics 10(3), 357–380 (2004)CrossRefGoogle Scholar
  9. 9.
    Cahon, S., Melab, N., Talbi, E.G.: An enabling framework for parallel optimization on the computational grid. In: CCGRID, pp. 702–709 (2005)Google Scholar
  10. 10.
    Cantu-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Norwell (2000)zbMATHGoogle Scholar
  11. 11.
    Cappello, F., Caron, E., Dayde, M., Desprez, F., Jegou, Y., Primet, P., Jeannot, E., Lanteri, S., Leduc, J., Melab, N., Mornet, G., Namyst, R., Quetier, B., Richard, O.: Grid’5000: a large scale and highly reconfigurable grid experimental testbed. In: The 6th IEEE/ACM International Workshop on Grid Computing, pp. 99–106 (2005)Google Scholar
  12. 12.
    Chen, C., Lin, J., Yücesan, E., Chick, S.E.: Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Journal of Discrete Event Dynamic Systems: Theory and Applications 10, 251–270 (2000)zbMATHCrossRefGoogle Scholar
  13. 13.
    Chen, C.H.: A lower bound for the correct subset-selection probability and its application to discrete event system simulations. IEEE Transactions on Automatic Control 41(8), 1227–1231 (1996)zbMATHCrossRefGoogle Scholar
  14. 14.
    Chick, S.E., Inoue, K.: New results on procedures that select the best system using crn. In: Simulation Conference Proceedings, vol. 1, pp. 554–561 (2000)Google Scholar
  15. 15.
    Cozzone, A.J.: Proteins: Fundamental chemical properties. Encyclopedia of Life Sciences, pp. 1–10. Macmillan Publishers Ltd, Nature Publishing Group (2002), www.els.net
  16. 16.
    Dauber-Osguthorpe, P., Roberts, V.A., Osguthorpe, D.J., Wolff, J., Genest, M., Hagler, A.T.: Structure and energetics of ligand binding to proteins: Escherichia coli dihydrofolate reductase-trimethoprim, a drug-receptor system. Proteins: Structure, Function, and Genetics 4(1), 31–47 (1988)CrossRefGoogle Scholar
  17. 17.
    Dill, K.A.: Theory for the folding and stability of globular proteins. Biochemistry 24(6), 1501–1509 (1985)CrossRefGoogle Scholar
  18. 18.
    Gropp, W.: Mpich2: A new start for mpi implementations. In: Proceedings of the 9th European PVM/MPI Users’ Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface, p. 7. Springer, London (2002)CrossRefGoogle Scholar
  19. 19.
    Gropp, W., Lederman, S.H., Lumsdaine, A., Lusk, E., Nitzberg, B., Saphir, W., Snir, M.: MPI: The Complete Reference, The MPI-2 Extensions, vol. 2. MIT Press, Cambridge (1998)Google Scholar
  20. 20.
    Heinrich, J.: A guide to the Pearson Type IV distribution (2004)Google Scholar
  21. 21.
    Herrera, F., Lozano, M., Verdegay, J.L.: Fuzzy connective based crossover operators to model genetic algorithms population diversity. Tech. Rep. DECSAI-95110, University of Granada (1995)Google Scholar
  22. 22.
    Herrera, F., Lozano, M., Verdegay, J.: Dynamic and heuristic fuzzy connectives-based crossover operators for controlling the diversity and convengence of real-coded genetic algorithms (1996)Google Scholar
  23. 23.
    Herrera, F., Lozano, M., Verdegay, J.L.: Fuzzy connectives based crossover operators to model genetic algorithms population diversity. Fuzzy Sets Syst. 92(1), 21–30 (1997)CrossRefGoogle Scholar
  24. 24.
    Herrera, F., Lozano, M., Sánchez, A.M.: A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study. International Journal of Intelligent Systems 18(3), 309–338 (2003)zbMATHCrossRefGoogle Scholar
  25. 25.
    Hestenes, M.R.: Iterative methods for solving linear equations. Report 52-9, NAML (1951); Reprinted in the Journal of Optimization Theory and Applications 11, 323–334 (1973)Google Scholar
  26. 26.
    Hestenes, M.R., Stiefel, E.: Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards 49(6), 409–436 (1952)zbMATHMathSciNetGoogle Scholar
  27. 27.
    Ingber, L.: Adaptive simulated annealing (ASA): Lessons learned. Control and Cybernetics 25, 33–54 (1996)zbMATHGoogle Scholar
  28. 28.
    Ingber, L.: Adaptive simulated annealing (asa) and path-integral (pathint) algorithms: Generic tools for complex systems. Tech. rep., Lester Ingber Research, Chicago, IL (2001)Google Scholar
  29. 29.
    Joy, S., Nair, P.S., Hariharan, R., Pillai, M.R.: Detailed comparison of the protein-ligand docking efficiencies of gold, a commercial package and arguslab, a licensable freeware. Silico Biology 6(6), 601–605 (2006)Google Scholar
  30. 30.
    Linlin Qiu, S.J.H.: Internal friction in the ultrafast folding of the tryptophan cage. Chemical Physics 1(312), 327–333 (2005)Google Scholar
  31. 31.
    Michalewicz, Z.: Genetic algorithms + data structures = evolution programs, 2nd edn. Springer, New York (1994)zbMATHGoogle Scholar
  32. 32.
    Morris, G.M., Goodsell, D.S., Halliday, R.S., Huey, R., Hart, W.E., Belew, R.K., Olson, A.J.: Automated docking using a lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry 19(14), 1639–1662 (1999)CrossRefGoogle Scholar
  33. 33.
    Nagahara, Y.: The PDF and CF of Pearson type IV distributions and the ML estimation of the parameters. Statistics & Probability Letters 43(3), 251–264 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  34. 34.
    Nagahara, Y.: A method of simulating multivariate nonnormal distributions by the pearson distribution system and estimation. Computational Statistics & Data Analysis 47(1), 1–29 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  35. 35.
    Neumaier, A.: Molecular modeling of proteins and mathematical prediction of protein structure. SIAM Review 39(3), 407–460 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  36. 36.
    Parent, B., Tantar, A., Melab, N., Talbi, E.G., Horvath, D.: Grid-based evolutionary strategies applied to the conformational sampling problem. In: IEEE Congress on Evolutionary Computation pp. 291–296 (2007)Google Scholar
  37. 37.
    Ponder, J.W., Case, D.A.: Force fields for protein simulations. Advances in Protein Chemistry 66, 27–85 (2003)CrossRefGoogle Scholar
  38. 38.
    Rabow, A.A., Scheraga, H.A.: Improved genetic algorithm for the protein folding problem by use of a Cartesian combination operator. Protein Sci. 5(9), 1800–1815 (1996)CrossRefGoogle Scholar
  39. 39.
    Rosin, C.D., Halliday, R.S., Hart, W.E., Belew, R.K.: A comparison of global and local search methods in drug docking. In: Bäck, T. (ed.) Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA 1997). Morgan Kaufmann, San Francisco (1997)Google Scholar
  40. 40.
    Schmidt, C., Branke, J., Chick, S.E.: Integrating techniques from statistical ranking into evolutionary algorithms. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 752–763. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  41. 41.
    Snow, C.D., Qiu, L., Du, D., Gai, F., Hagen, S.J., Pande, V.S.: Trp zipper folding kinetics by molecular dynamics and temperature-jump spectroscopy. Proc. Natl. Acad. Sci. U S A 101(12), 4077–4082 (2004)CrossRefGoogle Scholar
  42. 42.
    Stewart, C.A., Müller, M.S., Lingwall, M.: Progress towards petascale applications in biology: Status in 2006. In: Euro-Par Workshops, pp. 289–303 (2006)Google Scholar
  43. 43.
    Talbi, E.G.: A taxonomy of hybrid metaheuristics. Journal of Heuristics 8(5), 541–564 (2002)CrossRefGoogle Scholar
  44. 44.
    Tantar, A.A., Melab, N., Talbi, E.G.: A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction. Soft Computing 12(12), 1185–1198 (2008)zbMATHCrossRefGoogle Scholar
  45. 45.
    Tantar, A.A., Melab, N., Talbi, E.G., Parent, B., Horvath, D.: A parallel hybrid genetic algorithm for protein structure prediction on the computational grid. Future Generation Computer Systems (in press)Google Scholar
  46. 46.
    Tavares, J., Tantar, A.A., Melab, N., Talbi, E.G.: The influence of mutation on protein-ligand docking optimization: a locality analysis. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 589–598. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  47. 47.
    Thomsen, R.: Flexible ligand docking using evolutionary algorithms: investigating the effects of variation operators and local search hybrids. Biosystems 72(1-2), 57–73 (2003)CrossRefGoogle Scholar
  48. 48.
    Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3(2), 82–102 (1999)CrossRefGoogle Scholar
  49. 49.
    Zhu, C., Byrd, R.H., Lu, P., Nocedal, J.: Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans. Math. Softw. 23(4), 550–560 (1997)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alexandru-Adrian Tantar
    • 1
  • Nouredine Melab
    • 1
  • El-Ghazali Talbi
    • 1
  1. 1.INRIA Lille - Nord Europe, DOLPHIN Project Team, LIFL UMR USTL/CNRS 8022, Parc Scientifique de la Haute BorneVilleneuve d’Ascq CedexFrance

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