Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCB.2003.822958
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dc.titleIncremental multiple objective genetic algorithms
dc.contributor.authorChen, Q.
dc.contributor.authorGuan, S.-U.
dc.date.accessioned2014-06-17T02:53:10Z
dc.date.available2014-06-17T02:53:10Z
dc.date.issued2004-06
dc.identifier.citationChen, Q., Guan, S.-U. (2004-06). Incremental multiple objective genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34 (3) : 1325-1334. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCB.2003.822958
dc.identifier.issn10834419
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/56313
dc.description.abstractThis paper presents a new genetic algorithm approach to multiobjective optimization problems-incremental multiple objective genetic algorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages. First, an independent population is evolved to optimize one specific objective. Second, the better-performing individuals from the single-objective population evolved in the above stage and the multiobjective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multiobjective population, to which a multiobjective evolution based on the incremented objective set is applied. The experimental results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA, and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TSMCB.2003.822958
dc.sourceScopus
dc.subjectIncremental problem solving
dc.subjectMultiobjective genetic algorithm
dc.subjectMultiobjective optimization
dc.subjectMultiobjective problems
dc.subjectVector optimization
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TSMCB.2003.822958
dc.description.sourcetitleIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
dc.description.volume34
dc.description.issue3
dc.description.page1325-1334
dc.description.codenITSCF
dc.identifier.isiut000221578100002
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