Deconvolution of X-ray Diffraction Profiles Using Genetic Algorithms and Differential Evolution

Santos, Sidolina P.; Gomez-Pulido, Juan A.; Sanchez-Bajo, Florentino

http://hdl.handle.net/10400.8/14381

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Authors

Santos, Sidolina P.

Gomez-Pulido, Juan A.

Sanchez-Bajo, Florentino

Abstract(s)

Some optimization problems arise when X-ray diffraction profiles are used to determine the microcrystalline characteristics of materials, like the detection of diffraction peaks and the deconvolution process necessary to obtain the pure diffraction profile. After applying the genetic algorithms to solve satisfactorily the first problem, in this work we propose two evolutionary algorithms to solve the deconvolution problem. This optimization problem targets the objective of obtaining the profile that contains the microstructural characteristics of a material from the experimental data and instrumental effects. This is a complex problem, ill-conditioned, since not only there are many possible solutions, but also some of them lack physical sense. In order to avoid such circumstance, the regularization techniques are used, where the optimization of some of their parameters by means of intelligent computing permits to obtain the optimal solutions of the problem.

Description

Part of the book series: Lecture Notes in Computer Science (LNTCS,volume 9095).
BooK part in: Advances in Computational Intelligence 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Palma de Mallorca, Spain, June 10-12, 2015. Proceedings, Part II Conference proceedings, 2015
Conference city Palma de Mallorca
Conference date 10 June 2015 - 12 June 2015
Conference code 119669

Keywords

Deconvolution Differential evolution Diffraction profiles Genetic algorithms X-ray

URI

http://hdl.handle.net/10400.8/14381

Citation

Santos, S.P., Gomez-Pulido, J.A., Sanchez-Bajo, F. (2015). Deconvolution of X-ray Diffraction Profiles Using Genetic Algorithms and Differential Evolution. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_42