Technical Report Number
The hierarchical Bayesian Optimization Algorithm (hBOA) [24, 25] learns bit-strings by constructing explicit centralized models of a population and using them to generate new instances. This thesis is concerned with extending hBOA to learning open-ended program trees. The new system, BOA programming (BOAP), improves on previous probabilistic model building GP systems (PMBGPs) in terms of the expressiveness and open-ended ﬂexibility of the models learned, and hence control over the distribution of individuals generated. BOAP is studied empirically on a toy problem (learning linear functions) in various conﬁgurations, and further experimental results are presented for two real-world problems: prediction of sunspot time series, and human gene function inference.
Looks, Moshe and Loui, R. P., "Learning Computer Programs with the Bayesian Optimization Algorithm" Report Number: WUCSE-2005-23 (2005). All Computer Science and Engineering Research.
Permanent URL: http://dx.doi.org/10.7936/K7J964QN