Name
Abstract | ||
|---|---|---|
Evolutionary multiobjective optimization (EMO) is an active research area in the field of evolutionary computation. EMO algorithms are designed to find a non-dominated solution set that approximates the entire Pareto front of a multiobjective optimization ... |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICSMC.2009.5346168 | SMC |
Keywords | Field | DocType |
evolutionary multiobjective optimization,environmental system,evolutionary computation,entire pareto front,active research area,non-dominated solution set,emo algorithm,game model,multiobjective optimization,information competition,government policies,environmental economics,government regulation,data mining,regulation,artificial intelligence,government,media,game theory | Government regulation,Computer science,Communication channel,Public policy,Legislation,Artificial intelligence,Game theory,Industrial organization,Machine learning,Government | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianjun Huai | 1 | 0 | 0.34 |
| Xuexi Huo | 2 | 1 | 0.70 |