One of the reasons one anonymous reviewer from GPEM gave was that The performance of the GEP algorithm compared to GP seems too good to be true to me. As I really want to see other scientists using GEP in other applications, I decided to publish my paper on the web in order to make this powerful algorithm available to all. Remember, though, that there is a patent pending and GEP can not be used commercially. Gene expression programming uses character linear chromosomes composed of genes structurally organised in a head and a tail. The chromosomes encode expression trees which are the object of selection.
Comments are welcome.
Matthew J. Beal Gatsby Computational Neuroscience Unit University College London Variational approximations are becoming a widespread tool for Bayesian learning of graphical models. We provide some theoretical results for the variational updates in a very general family of conjugate-exponential graphical models. We show how the belief propagation and the junction tree algorithms can be used in the inference step of variational Bayesian learning. A revised version of this paper will appear in Advances in Neural Information Processing Systems 13, MIT Press. We appreciate your questions and comments by e-mail or at the workshop.