The effect of factor interactions in Plackett-Burman experimental designs: Comparison of Bayesian-Gibbs analysis and genetic algorithms

Autores
Magallanes, Jorge Federico; Olivieri, Alejandro Cesar
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A genetic algorithm has been developed in order to estimate not only the main effects but also the association of terms when analyzing the influence of experimental factors through a Plackett-Burman design of experiments. The results for a series of simulated systems as well as experimental examples show excellent agreement with a Bayesian-Gibbs approach. The Plackett-Burman design is usually employed for screening, but its performance depends on the assumption that the interaction effects are negligible. Simulations allow one to analyze the effect of increasing interactions on the significance of main factors when Plackett-Burman designs are processed by neglecting factor associations.
Fil: Magallanes, Jorge Federico. Comisión Nacional de Energía Atómica; Argentina
Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina
Materia
BAYESIAN-GIBBS ANALYSIS
EXPERIMENTAL DESIGN AND MODELING
FACTOR ASSOCIATIONS
GENETIC ALGORITHMS
PLACKETT-BURMAN DESIGNS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/128454
Acceder
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network_name_str CONICET Digital (CONICET)
spelling The effect of factor interactions in Plackett-Burman experimental designs: Comparison of Bayesian-Gibbs analysis and genetic algorithmsMagallanes, Jorge FedericoOlivieri, Alejandro CesarBAYESIAN-GIBBS ANALYSISEXPERIMENTAL DESIGN AND MODELINGFACTOR ASSOCIATIONSGENETIC ALGORITHMSPLACKETT-BURMAN DESIGNShttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1A genetic algorithm has been developed in order to estimate not only the main effects but also the association of terms when analyzing the influence of experimental factors through a Plackett-Burman design of experiments. The results for a series of simulated systems as well as experimental examples show excellent agreement with a Bayesian-Gibbs approach. The Plackett-Burman design is usually employed for screening, but its performance depends on the assumption that the interaction effects are negligible. Simulations allow one to analyze the effect of increasing interactions on the significance of main factors when Plackett-Burman designs are processed by neglecting factor associations.Fil: Magallanes, Jorge Federico. Comisión Nacional de Energía Atómica; ArgentinaFil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; ArgentinaElsevier Science2010-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/128454Magallanes, Jorge Federico; Olivieri, Alejandro Cesar; The effect of factor interactions in Plackett-Burman experimental designs: Comparison of Bayesian-Gibbs analysis and genetic algorithms; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 102; 1; 5-2010; 8-140169-7439CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0169743910000316info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2010.02.007info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:25:09Zoai:ri.conicet.gov.ar:11336/128454instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:25:10.112CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv The effect of factor interactions in Plackett-Burman experimental designs: Comparison of Bayesian-Gibbs analysis and genetic algorithms
title The effect of factor interactions in Plackett-Burman experimental designs: Comparison of Bayesian-Gibbs analysis and genetic algorithms
spellingShingle The effect of factor interactions in Plackett-Burman experimental designs: Comparison of Bayesian-Gibbs analysis and genetic algorithms
Magallanes, Jorge Federico
BAYESIAN-GIBBS ANALYSIS
EXPERIMENTAL DESIGN AND MODELING
FACTOR ASSOCIATIONS
GENETIC ALGORITHMS
PLACKETT-BURMAN DESIGNS
title_short The effect of factor interactions in Plackett-Burman experimental designs: Comparison of Bayesian-Gibbs analysis and genetic algorithms
title_full The effect of factor interactions in Plackett-Burman experimental designs: Comparison of Bayesian-Gibbs analysis and genetic algorithms
title_fullStr The effect of factor interactions in Plackett-Burman experimental designs: Comparison of Bayesian-Gibbs analysis and genetic algorithms
title_full_unstemmed The effect of factor interactions in Plackett-Burman experimental designs: Comparison of Bayesian-Gibbs analysis and genetic algorithms
title_sort The effect of factor interactions in Plackett-Burman experimental designs: Comparison of Bayesian-Gibbs analysis and genetic algorithms
dc.creator.none.fl_str_mv Magallanes, Jorge Federico
Olivieri, Alejandro Cesar
author Magallanes, Jorge Federico
author_facet Magallanes, Jorge Federico
Olivieri, Alejandro Cesar
author_role author
author2 Olivieri, Alejandro Cesar
author2_role author
dc.subject.none.fl_str_mv BAYESIAN-GIBBS ANALYSIS
EXPERIMENTAL DESIGN AND MODELING
FACTOR ASSOCIATIONS
GENETIC ALGORITHMS
PLACKETT-BURMAN DESIGNS
topic BAYESIAN-GIBBS ANALYSIS
EXPERIMENTAL DESIGN AND MODELING
FACTOR ASSOCIATIONS
GENETIC ALGORITHMS
PLACKETT-BURMAN DESIGNS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv A genetic algorithm has been developed in order to estimate not only the main effects but also the association of terms when analyzing the influence of experimental factors through a Plackett-Burman design of experiments. The results for a series of simulated systems as well as experimental examples show excellent agreement with a Bayesian-Gibbs approach. The Plackett-Burman design is usually employed for screening, but its performance depends on the assumption that the interaction effects are negligible. Simulations allow one to analyze the effect of increasing interactions on the significance of main factors when Plackett-Burman designs are processed by neglecting factor associations.
Fil: Magallanes, Jorge Federico. Comisión Nacional de Energía Atómica; Argentina
Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina
description A genetic algorithm has been developed in order to estimate not only the main effects but also the association of terms when analyzing the influence of experimental factors through a Plackett-Burman design of experiments. The results for a series of simulated systems as well as experimental examples show excellent agreement with a Bayesian-Gibbs approach. The Plackett-Burman design is usually employed for screening, but its performance depends on the assumption that the interaction effects are negligible. Simulations allow one to analyze the effect of increasing interactions on the significance of main factors when Plackett-Burman designs are processed by neglecting factor associations.
publishDate 2010
dc.date.none.fl_str_mv 2010-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/128454
Magallanes, Jorge Federico; Olivieri, Alejandro Cesar; The effect of factor interactions in Plackett-Burman experimental designs: Comparison of Bayesian-Gibbs analysis and genetic algorithms; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 102; 1; 5-2010; 8-14
0169-7439
CONICET Digital
CONICET
url http://hdl.handle.net/11336/128454
identifier_str_mv Magallanes, Jorge Federico; Olivieri, Alejandro Cesar; The effect of factor interactions in Plackett-Burman experimental designs: Comparison of Bayesian-Gibbs analysis and genetic algorithms; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 102; 1; 5-2010; 8-14
0169-7439
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0169743910000316
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2010.02.007
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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