How to compare many-objective algorithms under different settings of population and archive sizes.
Hisao IshibuchiYu SetoguchiHiroyuki MasudaYusuke NojimaPublished in: CEC (2016)
Keyphrases
- orders of magnitude
- learning algorithm
- computational efficiency
- information retrieval
- theoretical analysis
- recently developed
- pairwise
- significant improvement
- worst case
- computational complexity
- benchmark datasets
- face recognition
- clustering algorithm
- computationally expensive
- times faster
- image processing
- convergence rate
- multi objective evolutionary algorithms
- parameter choices