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

Anna Styrcz; Janusz Mrozek; Grzegorz Mazur

International Journal of Applied Mathematics and Computer Science (2011)

  • Volume: 21, Issue: 3, page 559-566
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Anna Styrcz, Janusz Mrozek, and Grzegorz Mazur. "A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization." International Journal of Applied Mathematics and Computer Science 21.3 (2011): 559-566. <http://eudml.org/doc/208070>.

@article{AnnaStyrcz2011,
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.},
author = {Anna Styrcz, Janusz Mrozek, Grzegorz Mazur},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {global optimization; memetic algorithm; molecular geometry},
language = {eng},
number = {3},
pages = {559-566},
title = {A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization},
url = {http://eudml.org/doc/208070},
volume = {21},
year = {2011},
}

TY - JOUR
AU - Anna Styrcz
AU - Janusz Mrozek
AU - Grzegorz Mazur
TI - A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization
JO - International Journal of Applied Mathematics and Computer Science
PY - 2011
VL - 21
IS - 3
SP - 559
EP - 566
AB - 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.
LA - eng
KW - global optimization; memetic algorithm; molecular geometry
UR - http://eudml.org/doc/208070
ER -

References

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