title: Space complexity of estimation of distribution algorithms.
abstract: In this paper, we investigate the space complexity of the Estimation of Distribution Algorithms (EDAs), a class of sampling-based variants of the genetic algorithm. By analyzing the nature of EDAs, we identify criteria that characterize the space complexity of two typical implementation schemes of EDAs, the factorized distribution algorithm and Bayesian network-based algorithms. Using random additive functions as the prototype, we prove that the space complexity of the factorized distribution algorithm and Bayesian network-based algorithms is exponential in the problem size even if the optimization problem has a very sparse interaction structure.
authors.affiliation: Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada T6G 2E8. ygao@cs.ualberta.ca
authors.forename: Yong
authors.initials: Y
authors.lastname: Gao
authors.forename: Joseph
authors.initials: J
authors.lastname: Culberson
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D015233:Models, Statistical