PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2011 | 21 | 3 | 559-566
Tytuł artykułu

A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A novel, neural network controlled, dynamic evolutionary algorithm is proposed for the purposes of molecular geometry optimization. The approach is tested for selected model molecules and some molecular systems of importance in biochemistry. The new algorithm is shown to compare favorably with the standard, statically parametrized memetic algorithm.
Rocznik
Tom
21
Numer
3
Strony
559-566
Opis fizyczny
Daty
wydano
2011
otrzymano
2010-08-14
poprawiono
2011-01-31
Twórcy
autor
  • Department of Computational Methods in Chemistry, Jagiellonian University, ul. R. Ingardena 3, 30-060 Cracow, Poland
  • Department of Computational Methods in Chemistry, Jagiellonian University, ul. R. Ingardena 3, 30-060 Cracow, Poland
  • Department of Computational Methods in Chemistry, Jagiellonian University, ul. R. Ingardena 3, 30-060 Cracow, Poland
Bibliografia
  • Adcock, S. (n.d.). Genetic algorithm utility library, http://gaul.sourceforge.net/.
  • Angeline, P. J. (1995). Adaptive and self-adaptive evolutionary computations, in M. Palaniswami, Y. Attikiouzel, R. Marks, D. Fogel and T. Fukuda (Eds.) Computational Intelligence: A Dynamic Systems Perspective, IEEE Press, Ann Arbor, MN, p. 152.
  • Bäck, T. (1993). Optimal mutation rates in genetic search, in S. Forrest (Ed.), Proceedings of the 5th International Conference on Genetic Algorithms, Morgan Kaufmann, San Francisco, CA, p. 2.
  • Cicirello, V.A. and Smith, S.F. (2000). Modeling GA performance for control parameter optimization, in L.D. Whitley, D.E. Goldberg, E. Cantú-Paz, L. Spector, I.C. Parmee and H.-G. Beyer (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO, Morgan Kaufmann, Las Vegas, NV, p. 235.
  • Clune, J., Goings, S., Punch, B. and Goodman, E. (2005). Investigations in meta-GAs: Panaceas or pipe dreams?, GECCO Workshops, Washington, DC, USA, p. 235.
  • Culberson, J.C. (1998). On the futility of blind search: An algorithmic view of 'no free lunch', Evolutionary Computation 6(2): 109.
  • de Landgraaf, W.A., Eiben, A.E. and Nannen, V. (2007). Parameter calibration using meta-algorithms, IEEE Congress on Evolutionary Computation, Singapore, p. 71.
  • Eiben, A.E., Hinterding, R. and Michalewicz, Z. (1999). Parameter control in evolutionary algorithms, IEEE Transactions on Evolutionary Computation 3(2): 124.
  • Floudas, C.A. and Pardalos, P. (Eds.) (2000). Optimization in Computational Chemistry and Molecular Biology, Nonconvex Optimization and Its Applications, Vol. 40, Springer, New York, NY.
  • Frisch, M.J., Trucks, G.W., Schlegel, H.B., Scuseria, G.E., Robb, M.A., Cheeseman, J.R., Montgomery, Jr., . J.A., Vreven, T., Kudin, K.N., Burant, J.C., Millam, J.M., Iyengar, S.S., Tomasi, J., Barone, V., Mennucci, B., Cossi, M., Scalmani, G., Rega, N., Petersson, G.A., Nakatsuji, H., Hada, M., Ehara, M., Toyota, K., Fukuda, R., Hasegawa, J., Ishida, M., Nakajima, T., Honda, Y., Kitao, O., Nakai, H., Klene, M., Li, X., Knox, J. E., Hratchian, H.P., Cross, J.B., Bakken, V., Adamo, C., Jaramillo, J., Gomperts, R., Stratmann, R.E., Yazyev, O., Austin, A.J., Cammi, R., Pomelli, C., Ochterski, J.W., Ayala, P.Y., Morokuma, K., Voth, G.A., Salvador, P., Dannenberg, J.J., Zakrzewski, V.G., Dapprich, S., Daniels, A.D., Strain, M.C., Farkas, O., Malick, D.K., Rabuck, A.D., Raghavachari, K., Foresman, J.B., Ortiz, J.V., Cui, Q., Baboul, A.G., Clifford, S., Cioslowski, J., Stefanov, B.B., Liu, G., Liashenko, A., Piskorz, P., Komaromi, I., Martin, R.L., Fox, D.J., Keith, T., Al-Laham, M.A., Peng, C.Y., Nanayakkara, A., Challacombe, M., Gill, P. M.W., Johnson, B., Chen, W., Wong, M.W., Gonzalez, C. and Pople, J.A. (n.d.). Gaussian 03, revision c.02. Gaussian, Inc., Wallingford, CT.
  • Harrison, R.W. (1993). Stiffness and energy conservation in molecular dynamics: An improved integrator, Journal of Computational Chemistry 14(9): 1112.
  • Harrison, R.W., Chatterjee, D. and Weber, I.T. (1995). Analysis of six protein structures predicted by comparative modeling techniques, Proteins: Structure Function and Genetics 23(4): 463.
  • Hendrickson, B. (1995). The molecule problem: Exploiting structure in global optimization, SIAM Journal of Optimization 5(4): 835.
  • Hertz, J., Krogh, A. and Palmer, R.G. (1991). Introduction to the Theory of Neural Computation, Addison-Wesley, Redwood City, CA.
  • Holland, J.H. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI.
  • Moscato, P. (1999). Memetic algorithms: A short introduction, in D. Corne, M. Dorigo and F. Glover (Eds.), New Ideas in Optimization, McGraw-Hill, London, p. 219.
  • Moscato, P. and Cotta, C. (2004). Memetic algorithms, Optimization Techniques in Engineering, Springer-Verlag, New York, NY, p. 53.
  • Nissen, S. (2003). Implementation of a fast artificial neural network library (FANN), http://fann.sf.net.
  • Phillips, J.C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R.D., Kale, L. and Schulten, K. (2005). Scalable molecular dynamics with NAMD, Journal of Computational Chemistry 26(16): 1781.
  • Pintér, J.D. (Ed.) (2006). Global Optimization, Nonconvex Optimization and Its Applications, Vol. 85, Springer, New York, NY.
  • Riedmiller, M. (1994). Rprop-Description and implementation details, Technical report, Institute for Logic, Complexity and Deduction Systems, University of Karlsruhe, Karlsruhe.
  • Schmidt, M.W., Baldridge, K.K., Boatz, J.A., Elbert, S.T., Gordon, M.S., Jensen, J.H., Koseki, S., Matsunaga, N., Nguyen, K.A., Su, S., Windus, T.L., Dupuis, M. and Montgomery, J.A. (1993). General atomic and molecular electronic structure system, Journal of Computational Chemistry 14(11): 1347.
  • Sierka, M., Döbler, J., Sauer, J., Santambrogio, G., Brümmer, M., Wöste, L., Janssens, E., Meijer, G. and Asmis, K.R. (2007). Unexpected structures of aluminum oxide clusters in the gas phase, Angewandte Chemie International Edition 46(18): 3372-5.
  • Spears, W.M. (1995). Adapting crossover in evolutionary algorithms, Proceedings of the 4th Annual Conference on Evolutionary Programming, San Diego, CA, USA, p. 367.
  • Spoel, D.V.D., Lindahl, E., Hess, B., Groenhof, G., Mark, A.E. and Berendsen, H.J.C. (2005). GROMACS: Fast, flexible, and free, Journal of Computational Chemistry 26(16): 1701-1718, http://dx.doi.org/10.1002/jcc.20291.
  • te Velde, G., Bickelhaupt, F.M., Baerends, E.J., Fonseca Guerra, C., van Gisbergen, S.J.A., Snijders, J.G. and Ziegler, T. (2001). Chemistry with ADF, Journal of Computational Chemistry 22(9): 931.
  • Unger, R. and Moult, J. (1993). Finding the lowest free energy conformation of a protein is an NP-hard problem: Proof and implications, Bulletin of Mathematical Biology 55(6): 1183-1198, http://dx.doi.org/10.1007/BF02460703.
  • Wales, D.J. (1999). Global optimization of clusters, crystals, and biomolecules, Science 285(5432): 1368.
  • Wolpert, D.H. and Macready, W.G. (1997). No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation 1(1): 67.
  • Wu, A.S., Lindsay, R.K. and Riolo, R.L. (1997). Empirical observations on the roles of crossover and mutation, in T. Bäck (Ed.), International Conference on Genetic Algorithms, ICGA, Morgan Kaufmann, San Francisco, CA, p. 362.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv21i3p559bwm
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.