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Related Questions
- Which Bayesian optimization algorithms are effective in mitigating the effect of dimensionality in MOO? For example, Expected Hypervolume Improvement (EHI), expected improvement, and others have been employed, but under which specific circumstances are one or a combination of the mentioned efficient in addressing HD?
- Are GP-BMs or Gaussian process surrogate models useful for high dimensional problems and do they inherently have built-in dimension reduction methods or would it still require further feature selection? What kind of hyper parameter tuning could reduce dimension while retaining essential structure of our function, leading to enhanced convergence efficiency of optimization outcomes?
- Is Eps-Mu-G-PS-3 more optimal in large d when having only m<20 evaluation budget (the common restriction), particularly concerning exploration with noise with our particular goal in which no derivatives to work around for high nonlinear, multilevel composite cost?
- Does PBN-based surrogation using random- and mixed-domain representations contribute better solutions through reducing and coping dimensions without needing human involvement such that convergence improved even considering m < 40 in complex composite function having more than 50,000 data points and less than 5 dimensions relevant to search?
- Which are the best, and/or combination of GP, PBBO, Surrogate assisted algorithms in low evaluation budgets to achieve most accurate or reasonable MO solutions, where more than two objectives in each scenario when d << n (high number of design variables), 30 ~ 60 search variables as per scenario but having over 1M training samples from our knowledge domain to ensure a much more stable training?
- Could there be room for adaptation and extension, particularly through better use of information theoretic or decision theory (information gain to choose variable order for new inputs as an example to optimize each decision tree)?
- How best can a knowledge gradient like strategy for a BO of the expected utility function handle a wide variety of uncertainties (different levels of prior knowledge of uncertainty and when that might vary, non-Gaussian, non-iid etc)?
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