@techreport{StorkZaeffererFischbachetal.2017,
  type      = {Working Paper},
  author    = {Stork, J{\"o}rg and Zaefferer, Martin and Fischbach, Andreas and Rehbach, Frederik and Bartz-Beielstein, Thomas},
  title     = {Surrogate-Assisted Learning of Neural Networks},
  institution = {Fakult{\"a}t f{\"u}r Informatik und Ingenieurwissenschaften (F10)},
  series    = {CIplus},
  number    = {8/2017},
  pages     = {22},
  year      = {2017},
  abstract  = {Surrogate-assisted optimization has proven to be very successful if applied to industrial problems. The use of a data-driven surrogate model of an objective function during an optimization cycle has many bene ts, such as being cheap to evaluate and further providing both information about the objective landscape and the parameter space. In preliminary work, it was researched how surrogate-assisted optimization can help to optimize the structure of a neural network (NN) controller. In this work, we will focus on how surrogates can help to improve the direct learning process of a transparent feed-forward neural network controller. As an initial case study we will consider a manageable real-world control task: the elevator supervisory group problem (ESGC) using a simplified simulation model. We use this model as a benchmark which should indicate the applicability and performance of surrogate-assisted optimization to this kind of tasks. While the optimization process itself is in this case not onsidered expensive, the results show that surrogate-assisted optimization is capable of outperforming metaheuristic optimization methods for a low number of evaluations. Further the surrogate can be used for signi cance analysis of the inputs and weighted connections to further exploit problem information.},
  language  = {en}
}