Name
Title | ||
|---|---|---|
Surrogate-Assisted Evolutionary Framework with Adaptive Knowledge Transfer for Multi-Task Optimization |
Abstract | ||
|---|---|---|
Multi-task optimization is a hot research topic in the field of evolutionary computation. This paper proposes an efficient surrogate-assisted multi-task evolutionary framework (named SaEF-AKT) with adaptive knowledge transfer for multi-task optimization. In the proposed SaEF-AKT, several tasks which are computationally expensive are solved jointly in each generation. Surrogate models are built bas... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TETC.2019.2945775 | IEEE Transactions on Emerging Topics in Computing |
Keywords | DocType | Volume |
Task analysis,Optimization,Knowledge transfer,Evolutionary computation,Sociology,Statistics,Benchmark testing | Journal | 9 |
Issue | ISSN | Citations |
4 | 2168-6750 | 5 |
PageRank | References | Authors |
0.41 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shijia Huang | 1 | 5 | 0.41 |
| Jing-hui Zhong | 2 | 380 | 33.00 |
| Wei-Jie Yu | 3 | 5 | 0.41 |