000075704 001__ 75704
000075704 005__ 20200117212658.0
000075704 0247_ $$2doi$$a10.1016/j.asoc.2017.10.042
000075704 0248_ $$2sideral$$a103510
000075704 037__ $$aART-2018-103510
000075704 041__ $$aeng
000075704 100__ $$0(orcid)0000-0003-2988-7728$$aMateo, P.M.$$uUniversidad de Zaragoza
000075704 245__ $$aGraph-based solution batch management for Multi-Objective Evolutionary Algorithms
000075704 260__ $$c2018
000075704 5060_ $$aAccess copy available to the general public$$fUnrestricted
000075704 5203_ $$aIn 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.
000075704 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E58$$9info:eu-repo/grantAgreement/ES/MINECO/MTM2016-77015-R
000075704 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000075704 590__ $$a4.873$$b2018
000075704 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b11 / 106 = 0.104$$c2018$$dQ1$$eT1
000075704 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b20 / 133 = 0.15$$c2018$$dQ1$$eT1
000075704 592__ $$a1.216$$b2018
000075704 593__ $$aSoftware$$c2018$$dQ1
000075704 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000075704 700__ $$0(orcid)0000-0003-3560-7550$$aAlberto, I.$$uUniversidad de Zaragoza
000075704 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000075704 773__ $$g62 (2018), 619-635$$pAppl. Soft. Comput.$$tAPPLIED SOFT COMPUTING$$x1568-4946
000075704 8564_ $$s379937$$uhttps://zaguan.unizar.es/record/75704/files/texto_completo.pdf$$yPostprint
000075704 8564_ $$s52616$$uhttps://zaguan.unizar.es/record/75704/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000075704 909CO $$ooai:zaguan.unizar.es:75704$$particulos$$pdriver
000075704 951__ $$a2020-01-17-21:24:01
000075704 980__ $$aARTICLE