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.2009.5346854 | SMC |
Keywords | Field | DocType |
particle swarm optimization,genetic algorithms,pattern recognition,nist,neural network,neural nets,data mining,gallium,genetic algorithm | Particle swarm optimization,Computer science,Tuple,NIST,Artificial intelligence,Classifier (linguistics),Artificial neural network,Genetic algorithm,Machine learning | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. A. Hannan Bin Azhar | 1 | 11 | 4.15 |
| Farzin Deravi | 2 | 296 | 36.61 |
| K. R. Dimond | 3 | 11 | 3.86 |