title: Evolutionary learning and hierarchical Markov systems.
abstract: The observation is made that various forms of evolutionary learning systems and classical evolutionary processes can be formally described as hierarchical systems of Markov processes. This leads to a simplification of issues such as convergence criteria and limiting behavior of such systems. The hierarchical structures in question are derived from the notion of rules and meta-rules for moving on graphs studied previously.
authors.affiliation: Department of Mathematics, Hofstra University, Hempstead, NY 11550.
authors.forename: H M
authors.initials: HM
authors.lastname: Hastings
authors.forename: S
authors.initials: S
authors.lastname: Waner
authors.forename: Y R
authors.initials: YR
authors.lastname: Wu
mesh_terms: D005075:Biological Evolution; D007858:Learning; D008390:Markov Chains; D008954:Models, Biological; D008962:Models, Theoretical; D009415:Nerve Net / Q000033:anatomy & histology / Q000502:physiology; D011336:Probability; D013269:Stochastic Processes