title: On the optimal convergence probability of univariate estimation of distribution algorithms.
abstract: In this paper we obtain bounds on the probability of convergence to the optimal solution for the compact genetic algorithm (cGA) and the population based incremental learning (PBIL). Moreover, we give a sufficient condition for convergence of these algorithms to the optimal solution and compute a range of possible values for algorithm parameters at which there is convergence to the optimal solution with a predefined confidence level.
authors.affiliation: Department of Mathematics, Iowa State University, Ames, Iowa 50011, USA. rastegar@iastate.edu
authors.forename: Reza
authors.initials: R
authors.lastname: Rastegar
mesh_terms: D000465:Algorithms; D005828:Genetics, Population / Q000379:methods; D008390:Markov Chains; D008957:Models, Genetic; D011336:Probability