title: Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks.
abstract: Many optimization problems are what can be called globally multimodal, i.e., they present several global optima. Unfortunately, this is a major source of difficulties for most estimation of distribution algorithms, making their effectiveness and efficiency degrade, due to genetic drift. With the aim of overcoming these drawbacks for discrete globally multimodal problem optimization, this paper introduces and evaluates a new estimation of distribution algorithm based on unsupervised learning of Bayesian networks. We report the satisfactory results of our experiments with symmetrical binary optimization problems.
authors.affiliation: Computational Biology, Dept. of Physics and Measurement Technology, Linköping University, Sweden. jmp@ifm.liu.se
authors.forename: J M
authors.initials: JM
authors.lastname: Peña
authors.forename: J A
authors.initials: JA
authors.lastname: Lozano
authors.forename: P
authors.initials: P
authors.lastname: Larrañaga
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D005075:Biological Evolution; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011336:Probability; D013269:Stochastic Processes