Investigating the Effect of Imbalance Between Convergence and Diversity in Evolutionary Multiobjective Algorithms.
Hai-Lin LiuLei ChenKalyanmoy DebErik D. GoodmanPublished in: IEEE Trans. Evol. Comput. (2017)
Keyphrases
- evolutionary multiobjective
- learning algorithm
- times faster
- orders of magnitude
- computationally efficient
- theoretical analysis
- convergence rate
- special case
- significant improvement
- worst case
- optimization problems
- computational cost
- data sets
- machine learning algorithms
- benchmark datasets
- data mining
- class imbalance
- global convergence
- stopping criteria
- neural network