Analysing the Robustness of NSGA-II under Noise
Duc-Cuong Dang, Andre Opris, Bahare Salehi, Dirk Sudholt · Proceedings of the Genetic and Evolutionary Computation Conference · 2023
Runtime analysis has produced many results on the efficiency of simple evolutionary algorithms like the (1+1) EA, and its analogue called GSEMO in evolutionary multiobjective optimisation (EMO). Recently, the first runtime analyses of the famous and highly cited EMO algorithm NSGA-II have emerged, demonstrating that practical algorithms with thousands of applications can be rigorously analysed. However, these results only show that NSGA-II has the same performance guarantees as GSEMO and it is unclear how and when NSGA-II can outperform GSEMO.