75704 20200117212658.0 doi 10.1016/j.asoc.2017.10.042 sideral 103510 ART-2018-103510 eng (orcid)0000-0003-2988-7728 Mateo, P.M. Universidad de Zaragoza Graph-based solution batch management for Multi-Objective Evolutionary Algorithms 2018 Access copy available to the general public Unrestricted In Alberto and Mateo [2], 2004, a graph-based structure used for manipulating populations of Multi-Objective Evolutionary Algorithms in a more efficient way than the structures existing at that point was defined. In this paper, an improvement of such tool is presented. It consists of the simultaneous insertion of a set of solutions (solution batch), instead of a single one, into the created graph structure. Furthermore, two experiments devoted to comparing the behavior of the new algorithms with the original version from Alberto and Mateo [2] and with a well-known non-dominated sorting algorithm are carried out. The first shows how the new version outperforms the original one in time and number of Pareto comparisons. The second experiment shows a reduction in the time needed in all the cases and an important reduction in the number of Pareto comparisons when inserting chains of dominated solutions. From these experiments it is verified that, in general, the new proposals save computational time and, in the majority of the cases, the number of Pareto comparisons carried out for the insertion. In addition, when the new proposals outperform the others, they increase their gain over them as the size of the population and/or the size of the batch increases. The new tool can also be used, for example, in parallel genetic algorithms such as the ones based on islands, to carry out the migrations of the solutions. info:eu-repo/grantAgreement/ES/DGA/E58 info:eu-repo/grantAgreement/ES/MINECO/MTM2016-77015-R info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 4.873 2018 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 11 / 106 = 0.104 2018 Q1 T1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 20 / 133 = 0.15 2018 Q1 T1 1.216 2018 Software 2018 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion (orcid)0000-0003-3560-7550 Alberto, I. Universidad de Zaragoza 2007 265 Universidad de Zaragoza Dpto. Métodos Estadísticos Área Estadís. Investig. Opera. 62 (2018), 619-635 Appl. Soft. Comput. APPLIED SOFT COMPUTING 1568-4946 379937 http://zaguan.unizar.es/record/75704/files/texto_completo.pdf Postprint 52616 http://zaguan.unizar.es/record/75704/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:75704 articulos driver 2020-01-17-21:24:01 ARTICLE