@techreport{Bartz-Beielstein2016,
  author    = {Bartz-Beielstein, Thomas},
  title     = {Stacked Generalization of Surrogate Models - A Practical Approach},
  institution = {Fakult{\"a}t f{\"u}r Informatik und Ingenieurwissenschaften (F10)},
  series    = {CIplus},
  number    = {5/2016},
  pages     = {20},
  year      = {2016},
  abstract  = {This report presents a practical approach to stacked generalization in surrogate model based optimization. It exemplifies the integration of stacking methods into the surrogate model building process. First, a brief overview of the current state in surrogate model based opti- mization is presented. Stacked generalization is introduced as a promising ensemble surrogate modeling approach. Then two examples (the first is based on a real world application and the second on a set of artificial test functions) are presented. These examples clearly illustrate two properties of stacked generalization: (i) combining information from two poor performing models can result in a good performing model and (ii) even if the ensemble contains a good performing model, combining its information with information from poor performing models results in a relatively small performance decrease only.},
  subject      = {Metamodell},
  language  = {en}
}