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dc.contributor.authorZhang Lilizh_CN
dc.contributor.authorZeng Wenhuazh_CN
dc.contributor.author张丽丽zh_CN
dc.contributor.author曾文华zh_CN
dc.date.accessioned2015-07-22T02:39:24Z
dc.date.available2015-07-22T02:39:24Z
dc.date.issued2008zh_CN
dc.identifier.citation2008 3rd International Conference on Intelligent System and Knowledge Engineering, Vols 1 and 2, 2008:502-507zh_CN
dc.identifier.otherWOS:000262437400096zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/86538
dc.descriptionConference Name:3rd International Conference on Intelligent System and Knowledge Engineering. Conference Address: Xiamen, PEOPLES R CHINA. Time:NOV 17-19, 2008.zh_CN
dc.description.abstractA large number of multi-objective optimization evolutionary algorithms(MOEAs) have been developed in the past two decades. To compare these methods rigorously, or to measure the performance of a particular MOEA quantitatively, a variety of performance measures have been proposed. In this paper, some existing widely-used performance measures are briefly reviewed and compared according different properties. Two new performance measures computing the convergence towards the Pareto front and the solution diversity on the Pareto front are proposed And an outlook on how to further deepen insight in performance measures of MOEAs is given.zh_CN
dc.language.isoen_USzh_CN
dc.publisherIEEEzh_CN
dc.source.urihttp://dx.doi.org/10.1109/ISKE.2008.4730983zh_CN
dc.titleResearch on Performance Measures of Multi-objective Optimization Evolutionary Algorithmszh_CN
dc.typeConferencezh_CN


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