Sequential Parameter Optimization An Annotated Bibliography
Thomas Bartz–Beielstein · 2010
This report collects more than one hundred publications related to the sequential parameter optimization, which was developed by Thomas Bartz-Beielstein, Christian Lasarczyk, and Mike Preuss over the last years. Sequential parameter optimization can be described as a tuning algorithm with the following properties: (i) Use the available budget (e.g., simulator runs, number of function evaluations) sequentially, i.e., use information from the exploration of the search space to guide the search by building one or several meta models. 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 information to learn from this tuning process, e.g., apply explorative data analysis. (iv) Provide mechanisms both for interactive and