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.
@article{bwmeta1.element.bwnjournal-article-amcv21i3p559bwm,
author = {Anna Styrcz and Janusz Mrozek and Grzegorz Mazur},
title = {A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization},
journal = {International Journal of Applied Mathematics and Computer Science},
volume = {21},
year = {2011},
pages = {559-566},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-amcv21i3p559bwm}
}
Anna Styrcz; Janusz Mrozek; Grzegorz Mazur. A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization. International Journal of Applied Mathematics and Computer Science, Tome 21 (2011) pp. 559-566. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-amcv21i3p559bwm/
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