Evolutionary Models applied to Multiprocessor TaskScheduling: Serial and Multipopulation Genetic Algorithm

dc.creatorDantas Morais, Bruno Well
dc.creatorBarbosa de Oliveira, Gina Maira
dc.creatorde Carvalho, Tiago Ismailer
dc.date2019-04-14
dc.date.accessioned2022-10-04T22:27:19Z
dc.date.available2022-10-04T22:27:19Z
dc.identifierhttps://seer.ufrgs.br/index.php/rita/article/view/RITA-VOL26-NR1-11
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3870383
dc.descriptionThis work presents the development of a multipopulation genetic algorithm for the task schedulingproblem with communication costs, aiming to compare its performance with the serial genetic algorithm. For thispurpose, a set of instances was developed and different approaches for genetic operations were compared.Experiments were conducted varying the number of populations and the number of processors available forscheduling. Solution quality and execution time were analyzed, and results show that the AGMP with adjustedparameters generally produces better solutions while requiring less execution time.en-US
dc.formatapplication/pdf
dc.languageeng
dc.publisherInstituto de Informática - Universidade Federal do Rio Grande do Sulen-US
dc.relationhttps://seer.ufrgs.br/index.php/rita/article/view/RITA-VOL26-NR1-11/pdf
dc.rightsCopyright (c) 2019 Bruno Well Dantas Morais, Gina Maira Barbosa de Oliveira, Tiago Ismailer de Carvalhopt-BR
dc.sourceRevista de Informática Teórica e Aplicada; Vol. 26 No. 1 (2019); 11-25en-US
dc.sourceRevista de Informática Teórica e Aplicada; v. 26 n. 1 (2019); 11-25pt-BR
dc.source2175-2745
dc.source0103-4308
dc.subjectmultipopulation genetic algorithmen-US
dc.subjectmultiprocessor task schedulingen-US
dc.titleEvolutionary Models applied to Multiprocessor TaskScheduling: Serial and Multipopulation Genetic Algorithmen-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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