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No Free Lunch, Bayesian Inference, And Utility: A Decision-Theoretic Approach To Optimization, Christopher Kenneth Monson
No Free Lunch, Bayesian Inference, And Utility: A Decision-Theoretic Approach To Optimization, Christopher Kenneth Monson
Theses and Dissertations
Existing approaches to continuous optimization are essentially mechanisms for deciding which locations should be sampled in order to obtain information about a target function's global optimum. These methods, while often effective in particular domains, generally base their decisions on heuristics developed in consideration of ill-defined desiderata rather than on explicitly defined goals or models of the available information that may be used to achieve them. The problem of numerical optimization is essentially one of deciding what information to gather, then using that information to infer the location of the global optimum. That being the case, it makes sense to model …