<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Department of Computational Methods in Chemistry, Jagiellonian University, ul. R. Ingardena 3, 30-060 Cracow, Poland

International Journal of Applied Mathematics and Computer Science

A novel, neural network controlled, dynamic evolutionary algorithm is proposed for the purposes of molecular geometry optimization. The approach is tested for...

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

Publié en ligne: 22 sept. 2011

Pages: 559 - 566

DOI: https://doi.org/10.2478/v10006-011-0044-8

Mots clés
global optimization, memetic algorithm, molecular geometry

This content is open access.

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

Publié en ligne: 22 sept. 2011

Pages: 559 - 566

DOI: https://doi.org/10.2478/v10006-011-0044-8

Mots clésglobal optimization, memetic algorithm, molecular geometry

This content is open access.

Mots clés
global optimization, memetic algorithm, molecular geometry