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Repositorio de la Universidad de Oviedo. > Producción Bibliográfica de UniOvi: RECOPILA > Artículos >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10651/25254

Title: A parallel genetic algorithm for optimizing an industrial inspection system
Author(s): González Bulnes, Francisco
Usamentiaga Fernández, Rubén
García Martínez, Daniel Fernando
Molleda Meré, Julio
Issue date: 2013
Publisher: IEEE
Publisher version: http://dx.doi.org/10.1109/TLA.2013.6710381
Citation: IEEE Latin America Transactions, 11(6), p. 1338-1343 (2013); doi:10.1109/TLA.2013.6710381
Format extent: p. 1338-1343
Abstract: Periodical defect detection is a task of great importance during the production of web materials. It can reduce the appearance of a large number of surface defects, which is of vital importance to keep the product quality. In this article, a system used to detect these defects is optimized. This is carried out by looking for the optimal values for each of its configuration parameters. Since the search space formed by these parameters is very large, it cannot be explored exhaustively. For this reason, an intelligent search, like genetic algorithms, must be used. Because the fitness function is computationally heavy, a single computer would take a long time to provide an acceptable solution. For this reason, a cluster of computers is used instead, running a parallel genetic algorithm. Thus, the optimal configuration could be determined in only a few hours
URI: http://hdl.handle.net/10651/25254
ISSN: 1548-0992
Local identifier: 20141115
Sponsored: HPC-EUROPA2 project (project number: 228398) with the support of the European Commission Capacities Area - Research Infrastructures Initiative. This work was partially supported by contracts with ArcelorMittal Corporation
Project id.: UE-HPC-EUROPA2/228398
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