SEMO declines in convergence speed on large-scale MOCOPs compared to NSGA-II, SMS-EMOA and MOEA/D, with absence of crossover as the main cause; adding crossover accelerates convergence at the expense of diversity.
Dominance, Indica- tor and Decomposition Based Search for Multi-objective QAP: Landscape Analysis and Automated Algorithm Selection, pages 33–47
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.NE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
On Scalability of Multi-Objective Evolutionary Algorithms on Combinatorial Optimisation Problems
SEMO declines in convergence speed on large-scale MOCOPs compared to NSGA-II, SMS-EMOA and MOEA/D, with absence of crossover as the main cause; adding crossover accelerates convergence at the expense of diversity.