Evolutionary learning and hierarchical Markov systems.
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.
Hastings
H M
HM
Department of Mathematics, Hofstra University, Hempstead, NY 11550.
Waner
S
S
Wu
Y R
YR
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