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

Anna Styrcz; Janusz Mrozek; Grzegorz Mazur

doi:10.2478/v10006-011-0044-8

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

International Journal of Applied Mathematics and Computer Science

Volume 21 (2011): Issue 3 (September 2011)

By: Anna Styrcz, Janusz Mrozek and Grzegorz Mazur

Open Access

|Sep 2011

Abstract

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.

References

Adcock, S. (n.d.). Genetic algorithm utility library, http://gaul.sourceforge.net/
Search in Google Scholar
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.
Search in Google Scholar
BéaUck, 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.
Search in Google Scholar
Cicirello, V.A. and Smith, S.F. (2000). Modeling GA performance for control parameter optimization, in L.D. Whitley, D.E. Goldberg, E. CantéuA-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.
Search in Google Scholar
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.
Search in Google Scholar
Culberson, J.C. (1998). On the futility of blind search: An algorithmic view of "no free lunch", Evolutionary Computation 6(2): 109.10.1162/evco.1998.6.2.10910021743
Search in Google Scholar
de Landgraaf, W.A., Eiben, A.E. and Nannen, V. (2007). Parameter calibration using meta-algorithms, IEEE Congress on Evolutionary Computation, Singapore, p. 71.
Search in Google Scholar
Eiben, A.E., Hinterding, R. and Michalewicz, Z. (1999). Parameter control in evolutionary algorithms, IEEE Transactions on Evolutionary Computation 3(2): 124.10.1109/4235.771166
Search in Google Scholar
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.
Search in Google Scholar
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.
Search in Google Scholar
Harrison, R.W. (1993). Stiffness and energy conservation in molecular dynamics: An improved integrator, Journal of Computational Chemistry 14(9): 1112.10.1002/jcc.540140912
Search in Google Scholar
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): 463.10.1002/prot.3402304028749843
Search in Google Scholar
Hendrickson, B. (1995). The molecule problem: Exploiting structure in global optimization, SIAM Journal of Optimization 5(4): 835.10.1137/0805040
Search in Google Scholar
Hertz, J., Krogh, A. and Palmer, R.G. (1991). Introduction to the Theory of Neural Computation, Addison-Wesley, Redwood City, CA.10.1063/1.2810360
Search in Google Scholar
Holland, J.H. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI.
Search in Google Scholar
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.
Search in Google Scholar
Moscato, P. and Cotta, C. (2004). Memetic algorithms, Optimization Techniques in Engineering, Springer-Verlag, New York, NY, p. 53.
Search in Google Scholar
Nissen, S. (2003). Implementation of a fast artificial neural network library (FANN), http://fann.sf.net
Search in Google Scholar
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): 1781.10.1002/jcc.20289248633916222654
Search in Google Scholar
PintéeAr, J.D. (Ed.) (2006). Global Optimization, Nonconvex Optimization and Its Applications, Vol. 85, Springer, New York, NY.
Search in Google Scholar
Riedmiller, M. (1994). Rprop—Description and implementation details, Technical report, Institute for Logic, Complexity and Deduction Systems, University of Karlsruhe, Karlsruhe.
Search in Google Scholar
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): 1347.10.1002/jcc.540141112
Search in Google Scholar
Sierka, M., DéoUbler, J., Sauer, J., Santambrogio, G., BréuUmmer, M., WéoUste, 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): 3372-5.10.1002/anie.20060482317385778
Search in Google Scholar
Spears, W.M. (1995). Adapting crossover in evolutionary algorithms, Proceedings of the 4th Annual Conference on Evolutionary Programming, San Diego, CA, USA, p. 367.
Search in Google Scholar
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.20291.10.1002/jcc.2029116211538
Search in Google Scholar
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): 931.10.1002/jcc.1056
Search in Google Scholar
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/BF02460703.10.1007/BF024607038281131
Search in Google Scholar
Wales, D.J. (1999). Global optimization of clusters, crystals, and biomolecules, Science 285(5432): 1368.10.1126/science.285.5432.136810464088
Search in Google Scholar
Wolpert, D.H. and Macready, W.G. (1997). No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation 1(1): 67.10.1109/4235.585893
Search in Google Scholar
Wu, A.S., Lindsay, R.K. and Riolo, R.L. (1997). Empirical observations on the roles of crossover and mutation, in T. BéaUck (Ed.), International Conference on Genetic Algorithms, ICGA, Morgan Kaufmann, San Francisco, CA, p. 362.
Search in Google Scholar