Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/68898
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dc.titleA multi-objective genetic algorithm with controllable convergence on knee regions
dc.contributor.authorRachmawati, L.
dc.contributor.authorSrinivasan, D.
dc.date.accessioned2014-06-19T02:54:30Z
dc.date.available2014-06-19T02:54:30Z
dc.date.issued2006
dc.identifier.citationRachmawati, L.,Srinivasan, D. (2006). A multi-objective genetic algorithm with controllable convergence on knee regions. 2006 IEEE Congress on Evolutionary Computation, CEC 2006 : 1916-1923. ScholarBank@NUS Repository.
dc.identifier.isbn0780394879
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/68898
dc.description.abstractA knee region on the Pareto-optimal front of a multi-objective optimization problem consists of solutions with the maximum marginal rates of return, i.e. solutions for which an improvement on one objective is accompanied by a severe degradation in another. The trade-off characteristic renders such solutions of particular interest in practical applications. This paper presents a multi-objective evolutionary algorithm focused on the knee regions. The algorithm facilitates better decision making in contexts where high marginal rates of return are desirable by providing the Decision Makers with a high concentration of solutions on the knee regions of the Paretofront approximation. The proposed approach computes a transformation of the original objectives based on weighted-sum functions. The transformed functions identify niches which correspond to knee regions in the objective space. The extent and density of coverage of the knee regions are controllable by the niche strength and pool size parameters. Although based on weighted-sums, the algorithm is capable of finding solutions in the non-convex regions of the Paretofront. The application of the algorithm on test problems with multiple knee regions and skew on the Pareto-optimal front produces promising results. © 2006 IEEE.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.sourcetitle2006 IEEE Congress on Evolutionary Computation, CEC 2006
dc.description.page1916-1923
dc.identifier.isiutNOT_IN_WOS
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