Ensemble Based Optimization and Tuning Algorithms

i) Use the available budget (e.g., simulator runs, number of function evaluations) sequentially, i.e., use information from search-space exploration to guide the search by building one or several meta models, e.g., random forest, linear regression, or Kriging. Choose new design points based on predictions from the meta model(s). Refine the meta model(s) stepwise to improve knowledge about the search space. ii) Try to cope with noise by improving confidence. Guarantee comparable confidence for search points. iii) Collect and report tuning process information for exploratory data analysis. iv) Provide mechanisms both for interactive and automated tuning.