Improved runtime bounds for the univariate marginal distribution algorithm via anti-concentration

Unlike traditional evolutionary algorithms which produce offspring via genetic operators, Estimation of Distribution Algorithms (EDAs) sample solutions from probabilistic models which are learned from selected individuals. It is hoped that ED As may improve optimisation performance on epistatic fitness landscapes by learning variable interactions.