Level-Based Analysis of the Univariate Marginal Distribution Algorithm
Level-Based Analysis of the Univariate Marginal Distribution Algorithm is a scholarly work, published in 2018 in ''Algorithmica''. The main subjects of the publication include binary logarithm, Λ, Bayesian network, upper and lower bounds, algorithm, Estimation of distribution algorithm, active learning, mathematical optimization, discrete mathematics, combinatorics, mathematics, EDAS, and population. The authors show that the recently developed level-based theorem for non-elitist populations combined with anti-concentration results yield upper bounds on the expected optimisation time of the UMDA.