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A parallel genetic algorithm for optimizing an industrial inspection system

dc.contributor.authorGonzález Bulnes, Francisco 
dc.contributor.authorUsamentiaga Fernández, Rubén 
dc.contributor.authorGarcía Martínez, Daniel Fernando 
dc.contributor.authorMolleda Meré, Julio
dc.identifier.citationIEEE Latin America Transactions, 11(6), p. 1338-1343 (2013); doi:10.1109/TLA.2013.6710381
dc.description.abstractPeriodical 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
dc.description.sponsorshipHPC-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
dc.format.extentp. 1338-1343
dc.relation.ispartofIEEE Latin America Transactions, 11(6)
dc.rights© 2013 IEEE
dc.titleA parallel genetic algorithm for optimizing an industrial inspection systemeng

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