Exploratory data modeling with Bayesian-driven evolutionary search

We present a methodology for exploratory data modeling that combines evolutionary search with two levels of statistical inference provided by Bayesian interpolation (MacKay, 1992). Evolutionary methods are used to search in a space of model structures, whereas Bayesian interpolation is used to infer parameter values for candidate models as well as to evaluate the relative fitness of these models for guiding evolutionary search. We restrict ourselves to models that are linear in the parameters with polynomial terms; this class of models allows for a natural binary representation of model structures that promotes efficient evolutionary search. We demonstrate the ability of this methodology to find plausible models which handle a wide range of data conditions, including noisy and/or sparse data