Mean square error criteria to multiresponse process optimization by a new genetic algorithm

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dc.authorid0000-0002-8291-1419
dc.contributor.authorKoksoy, O
dc.contributor.authorYalcinoz, T
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2006
dc.departmentNiğde ÖHÜ
dc.description.abstractThe recent push for quality in industry has brought response surface methodology to the attention of many users. Most of the published literature on robust design methodology is basically concerned with optimization of a single response or quality characteristic which is often most critical to consumers. For most products, however, quality is multidimensional, so it is common to observe multiple responses in an experimental situation. In this paper, we present a methodology for analyzing several quality characteristics simultaneously using the mean square error (MSE) criterion when data are collected from a combined array. Problems with highly nonlinear, or multimodal, objective functions are extremely difficult to solve and are further complicated by the presence of multiple objectives. An alternative approach is to use a heuristic search procedure such as a genetic algorithm (GA). The GA generates a string of solutions using genetics-like operators such as selection, crossover and mutation. In this paper, a genetic algorithm based on arithmetic crossover for the multiresponse problem is proposed. The string of solutions highlight the trade-offs that one needs to consider in order to obtain a compromise solution. A numerical example has been presented to illustrate the performance and the applicability of the proposed method. (c) 2005 Elsevier Inc. All rights reserved.
dc.identifier.doi10.1016/j.amc.2005.09.011
dc.identifier.endpage1674
dc.identifier.issn0096-3003
dc.identifier.issue2
dc.identifier.scopus2-s2.0-33645853496
dc.identifier.scopusqualityQ1
dc.identifier.startpage1657
dc.identifier.urihttps://dx.doi.org/10.1016/j.amc.2005.09.011
dc.identifier.urihttps://hdl.handle.net/11480/5524
dc.identifier.volume175
dc.identifier.wosWOS:000237568000054
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherELSEVIER SCIENCE INC
dc.relation.ispartofAPPLIED MATHEMATICS AND COMPUTATION
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectmultiresponse process optimization
dc.subjectgenetic algorithm
dc.subjectquality improvement
dc.subjectrobust design
dc.subjectmean square error
dc.subjectcombined array
dc.subjectexperimental design
dc.titleMean square error criteria to multiresponse process optimization by a new genetic algorithm
dc.typeArticle

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