Guiding Evolutionary Multiobjective Optimization With Generic Front Modeling is a research paper published in IEEE Transactions on Cybernetics (2020). On theSindex it has a DataRank of 3.5. It has been cited 93 times, with 85 citing works in its 1-hop citation network.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
Base Score Contribution
0.681
From this paper's citation signal
Citation Network Contribution
2.9
From 67 citing papers with measurable signal
National Natural Science Foundation of China
Grant: 61822301
National Natural Science Foundation of China
Grant: 61672033
National Natural Science Foundation of China
Grant: 61502004
National Natural Science Foundation of China
Grant: 61502001
Natural Science Foundation of Anhui Province
Grant: 1808085J06
Shenzhen Peacock Plan
Grant: KQTD2016112514355531
Engineering and Physical Sciences Research Council
Grant: EP/M017869/1
Data-Driven Surrogate-Assisted Evolutionary Fluid Dynamic Optimisation
FWCI
4.74
Citation Percentile
1.0%
Citation Trend
Fields of Study
Keywords
Sustainable Development Goals