南方科技大学知识苑(SUSTech KC): Non-elitist evolutionary multi-objective optimizers revisited

题名	Non-elitist evolutionary multi-objective optimizers revisited
作者	Tanabe, Ryoji; Ishibuchi, Hisao
通讯作者	Ishibuchi, Hisao
DOI	10.1145/3321707.3321754
发表日期	2019
会议录名称	2019 Genetic and Evolutionary Computation Conference, GECCO 2019
页码	612-619
会议地点	Prague, Czech republic
出版地	1515 BROADWAY, NEW YORK, NY 10036-9998 USA
出版者	Association for Computing Machinery, Inc
摘要	Since around 2000, it has been considered that elitist evolutionary multi-objective optimization algorithms (EMOAs) always outperform non-elitist EMOAs. This paper revisits the performance of non-elitist EMOAs for bi-objective continuous optimization when using an unbounded external archive. This paper examines the performance of EMOAs with two elitist and one non-elitist environmental selections. The performance of EMOAs is evaluated on the bi-objective BBOB problem suite provided by the COCO platform. In contrast to conventional wisdom, results show that non-elitist EMOAs with particular crossover methods perform significantly well on the bi-objective BBOB problems with many decision variables when using the unbounded external archive. This paper also analyzes the properties of the non-elitist selection. © 2019 Association for Computing Machinery.
关键词	Evolutionary multi-objective optimization continuous optimization non-elitist environmental selections
学校署名	第一 ; 通讯
语种	英语
相关链接	[来源记录]
收录类别	EI ; CPCI-S
资助项目	National Natural Science Foundation of China[61876075]
WOS研究方向	Computer Science ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Operations Research & Management Science
WOS记录号	WOS:000523218400073
EI入藏号	20193807458977
EI主题词	Computer science ; Evolutionary algorithms
EI分类号	Optimization Techniques:921.5
来源库	EV Compendex
引用统计	被引频次[WOS]：2
成果类型	会议论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/50848
专题	工学院_计算机科学与工程系
作者单位	Shenzhen Key Laboratory of Computational Intelligence, University Key Laboratory of Evolving Intelligent Systems of Guangdong Province, Department of Computer Science and Engineering, Southern University of Science and Technology, China
第一作者单位	计算机科学与工程系
通讯作者单位	计算机科学与工程系
第一作者的第一单位	计算机科学与工程系
推荐引用方式 GB/T 7714	Tanabe, Ryoji,Ishibuchi, Hisao. Non-elitist evolutionary multi-objective optimizers revisited[C]. 1515 BROADWAY, NEW YORK, NY 10036-9998 USA:Association for Computing Machinery, Inc,2019:612-619.