More precise runtime analyses of non-elitist EAs in uncertain environments
Per Kristian Lehre, Xiaoyu Qin · Proceedings of the Genetic and Evolutionary Computation Conference · 2021
Real-world optimisation problems often involve uncertainties. In the past decade, several rigorous analysis results for evolutionary algorithms (EAs) on discrete problems show that EAs can cope with low-level uncertainties, and sometimes benefit from uncertainties. Using non-elitist EAs with large population size is a promising approach to handle higher levels of uncertainties. However, the performance of non-elitist EAs in some common fitness-uncertainty scenarios is still unknown.