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oai:riubu.ubu.es:10259/42832022-04-29T12:02:48Zcom_10259_4249com_10259_5086com_10259_2604col_10259_4250

Sánchez Pastor, Mª Sagrario 518 500 Sarabia Peinador, Luis Antonio 534 500 Ortiz Fernández, Mª Cruz 406 500 2016-11-24T09:14:18Z 2016-11-24T09:14:18Z 2012-11 http://hdl.handle.net/10259/4283 Experimental designs for a given task should be selected on the base of the problem being solved and of some criteria that measure their quality. There are several such criteria because there are several aspects to be taken into account when making a choice. The most used criteria are probably the so-called alphabetical optimality criteria (for example, the A-, E-, and D-criteria related to the joint estimation of the coefficients, or the I- and G-criteria related to the prediction variance). Selecting a proper design to solve a problem implies finding a balance among these several criteria that measure the performance of the design in different aspects. Technically this is a problem of multi-criteria optimization, which can be tackled from different views. The approach presented here addresses the problem in its real vector nature, so that ad hoc experimental designs are generated with an algorithm based on evolutionary algorithms to find the Pareto-optimal front. There is not theoretical limit to the number of criteria that can be studied and, contrary to other approaches, no just one experimental design is computed but a set of experimental designs all of them with the property of being Pareto-optimal in the criteria needed by the user. Besides, the use of an evolutionary algorithm makes it possible to search in both continuous and discrete domains and avoid the need of having a set of candidate points, usual in exchange algorithms. Projects CTQ2011-26022(SpanishMinisteriodeEconomíayCompetitividad)andBU108A11-2(JuntadeCastillayLeón). application/pdf eng Elsevier Analytica Chimica Acta Volume 754, 19 November 2012, Pages 39–46 http://dx.doi.org/10.1016/j.aca.2012.10.014 Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Chemistry Mathematics Química Matemáticas On the construction of experimental designs for a given task by jointly optimizing several quality criteria: Pareto-optimal experimental designs info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 805 https://riubu.ubu.es/bitstream/10259/4283/8/license_rdf 4460e5956bc1d1639be9ae6146a50347 MD5 8 THUMBNAIL sánchez-ACA_2012.pdf.jpg sánchez-ACA_2012.pdf.jpg IM Thumbnail image/jpeg 3395 https://riubu.ubu.es/bitstream/10259/4283/7/s%c3%a1nchez-ACA_2012.pdf.jpg fad55147e6df50a39200d740ce3a463d MD5 7 ORIGINAL sánchez-ACA_2012.pdf sánchez-ACA_2012.pdf application/pdf 501182 https://riubu.ubu.es/bitstream/10259/4283/1/s%c3%a1nchez-ACA_2012.pdf fbe68b74c7795697bc171af7b8c9f667 MD5 1 LICENSE license.txt license.txt text/plain; charset=utf-8 998 https://riubu.ubu.es/bitstream/10259/4283/5/license.txt aa90105e1c90a1b4fe810649aa967b7d MD5 5 TEXT sánchez-ACA_2012.pdf.txt sánchez-ACA_2012.pdf.txt Extracted text text/plain 48796 https://riubu.ubu.es/bitstream/10259/4283/6/s%c3%a1nchez-ACA_2012.pdf.txt 36616123f9739dbfa600bdbdea0a3326 MD5 6 10259/4283 oai:riubu.ubu.es:10259/4283 2022-04-29 14:02:48.587 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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