Ensemble-Based Modeling
Thomas Bartz–Beielstein, Martina Friese, Oliver Flasch, Wolfgang Konen, Patrick N. Koch, Boris Naujoks · 2011
Sequential parameter optimization (SPO) can be described as a tuning algorithm with the following properties[Bartz-Beielstein et al., 2004]: (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. The SPO toolbox (SPOT) provides standardized interfaces, which enable the integration of several meta models in a convenient manner [Bartz-Beielstein et al., 2010]. 1 Naturally, the question arises, which meta model should be used during the tuning process. Instead of recommending one meta model only, we will analyze an alternative approach: Set up several models in parallel, and provide an effective and efficient policy for dynamical model selection. Goal of this study: How to dynamically select the right meta model amongst an ensemble of meta