dc.contributor.authorSeah, Chun-Wei
dc.contributor.authorOng, Yew Soon
dc.contributor.authorTsang, Ivor Wai-Hung
dc.contributor.authorJiang, Siwei
dc.date.accessioned2013-07-23T02:46:47Z
dc.date.available2013-07-23T02:46:47Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationSeah, C.-W., Ong, Y.-S., Tsang, I. W., & Jiang, S. (2012). Pareto Rank Learning in Multi-objective Evolutionary Algorithms. 2012 IEEE Congress on Evolutionary Computation (CEC).en_US
dc.identifier.urihttp://hdl.handle.net/10220/12018
dc.description.abstractIn this paper, the interest is on cases where assessing the goodness of a solution for the problem is costly or hazardous to construct or extremely computationally intensive to compute. We label such category of problems as “expensive” in the present study. In the context of multi-objective evolutionary optimizations, the challenge amplifies, since multiple criteria assessments, each defined by an “expensive” objective is necessary and it is desirable to obtain the Pareto-optimal solution set under a limited resource budget. To address this issue, we propose a Pareto Rank Learning scheme that predicts the Pareto front rank of the offspring in MOEAs, in place of the “expensive” objectives when assessing the population of solutions. Experimental study on 19 standard multi-objective benchmark test problems concludes that Pareto rank learning enhanced MOEA led to significant speedup over the state-of-the-art NSGA-II, MOEA/D and SPEA2.en_US
dc.language.isoenen_US
dc.rights© 2012 IEEE.en_US
dc.subjectDRNTU::Engineering::Computer science and engineering
dc.titlePareto rank learning in multi-objective evolutionary algorithmsen_US
dc.typeConference Paper
dc.contributor.conferenceIEEE Congress on Evolutionary Computation (2012 : Brisbane, Australia)en_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/CEC.2012.6252865


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