| Title | Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation |
| Author | |
| Publication Years | 2022
|
| DOI | |
| Source Title | |
| ISSN | 1941-0026
|
| Volume | PPIssue:99Pages:1-1 |
| Keywords | |
| URL | [Source Record] |
| SUSTech Authorship | First
|
| Data Source | IEEE
|
| PDF url | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9993794 |
| Citation statistics |
Cited Times [WOS]:0
|
| Document Type | Journal Article |
| Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/419361 |
| Department | Department of Computer Science and Engineering |
| Affiliation | Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China |
| First Author Affilication | Department of Computer Science and Engineering |
| First Author's First Affilication | Department of Computer Science and Engineering |
| Recommended Citation GB/T 7714 |
Ke Shang,Tianye Shu,Hisao Ishibuchi. Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation[J]. IEEE Transactions on Evolutionary Computation,2022,PP(99):1-1.
|
| APA |
Ke Shang,Tianye Shu,&Hisao Ishibuchi.(2022).Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation.IEEE Transactions on Evolutionary Computation,PP(99),1-1.
|
| MLA |
Ke Shang,et al."Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation".IEEE Transactions on Evolutionary Computation PP.99(2022):1-1.
|
| Files in This Item: | There are no files associated with this item. | |||||
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