Santos, Sidolina P.Gomez-Pulido, Juan A.Sanchez-Bajo, Florentino2025-10-272025-10-272015-06Santos, 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_42978331919221597833191922220302-97431611-3349http://hdl.handle.net/10400.8/14381Part 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, 2015Conference city Palma de MallorcaConference date 10 June 2015 - 12 June 2015Conference code 119669Some 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.engDeconvolutionDifferential evolutionDiffraction profilesGenetic algorithmsX-raybook part10.1007/978-3-319-19222-2_42