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.2009.5346311 | SMC |
Keywords | Field | DocType |
dynamic decision,evolutionary multiobjective optimization,evolutionary computation,entire pareto front,active research area,non-dominated solution set,emo algorithm,negative predictive power,multiobjective optimization,mathematical analysis,human machine system,chromium,signal detection,mathematical model,signal detection theory,tin,dynamic decision making | Human–machine system,Optimal decision,Predictive power,Detection theory,Computer science,Dynamic decision-making,Robust decision-making,Artificial intelligence,Machine learning | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| D. W. Repperger | 1 | 54 | 6.53 |
| Joel S. Warm | 2 | 199 | 29.80 |
| M. A. Vidulich | 3 | 0 | 0.34 |
| V. S. Finomore | 4 | 0 | 0.34 |