Stu Kauffman and I have addressed optimization issues in the burgeoning field of molecular evolution. We have investigated the suitability of the NK model to describe real molecular fitness landscapes and proposed new experimentally realizable optimization procedures. The Hubbard model is widely believed to hold the key to high temperature superconductivity. With David Wolpert I am extending the bagging technique of machine learning. By weighting each generalizer according to its off-replicate error we have been able to substantially lower the error of the bagged generalizer.
Work is underway in developing an approximate theory of landscapes.
I use a kind of controlled annealed approximation where a landscape is viewed as a joint probability distribution. I'd like to get to Investigation of the aging phenomena seen in spin glasses and its implications for optimization. Interesting work has shown that intuitions about flow energy landscapes developed from low dimensional problems are highly misleading in high dimensions Applications of wavelets to measures of complexity I am also working towards a mathematicization of artificial worlds. The mathematical formalism I have focused on is a mobile process algebra called pi calculus .