Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes.
Hisao IshibuchiYu SetoguchiHiroyuki MasudaYusuke NojimaPublished in: IEEE Trans. Evol. Comput. (2017)
Keyphrases
- multi objective
- orders of magnitude
- computational efficiency
- theoretical analysis
- data structure
- benchmark problems
- benchmark datasets
- machine learning algorithms
- computationally efficient
- computational cost
- significant improvement
- search algorithm
- times faster
- learning algorithm
- shape matching
- decomposition algorithm
- decomposition methods