@inbook{46381,
  abstract     = {{Exploratory Landscape Analysis is an effective and sophisticated approach to characterize the properties of continuous optimization problems. The overall aim is to exploit this knowledge to give recommendations of the individually best suited algorithm for unseen optimization problems. Recent research revealed a high potential of this methodology in this respect based on a set of well-defined, computable features which only requires a quite small sample of function evaluations. In this paper, new features based on the cell mapping concept are introduced and shown to improve the existing feature set in terms of predicting expert-designed high-level properties, such as the degree of multimodality or the global structure, for 2-dimensional single objective optimization problems.}},
  author       = {{Kerschke, Pascal and Preuss, Mike and Hernández, Carlos and Schütze, Oliver and Sun, Jian-Qiao and Grimme, Christian and Rudolph, Günter and Bischl, Bernd and Trautmann, Heike}},
  booktitle    = {{EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V}},
  editor       = {{Tantar, Alexandru-Adrian and Tantar, Emilia and Sun, Jian-Qiao and Zhang, Wei and Ding, Qian and Schütze, Oliver and Emmerich, Michael T M and Legrand, Pierrick and Del, Moral Pierre and Coello, Coello Carlos A}},
  isbn         = {{978-3-319-07493-1}},
  pages        = {{115–131}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Cell Mapping Techniques for Exploratory Landscape Analysis}}},
  doi          = {{10.1007/978-3-319-07494-8_9}},
  volume       = {{288}},
  year         = {{2014}},
}

@inbook{46382,
  abstract     = {{The incorporation of expert knowledge into multiobjective optimization is an important issue which in this paper is reflected in terms of an aspiration set consisting of multiple reference points. The behaviour of the recently introduced evolutionary multiobjective algorithm AS-EMOA is analysed in detail and comparatively studied for bi-objective optimization problems w.r.t. R-NSGA2 and a respective variant. It will be shown that the averaged Hausdorff distance, integrated into AS-EMOA, is an efficient means to accurately approximate the desired aspiration set.}},
  author       = {{Rudolph, G and Schütze, O and Grimme, C and Trautmann, Heike}},
  booktitle    = {{EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V}},
  editor       = {{Tantar, A and Tantar, E and Sun, J and Zhang, W and Ding, Q and Schütze, O and Emmerich, M and Legrand, P and Del, Moral P and Coello, Coello CA}},
  isbn         = {{978-3-319-07493-1}},
  pages        = {{261–273}},
  publisher    = {{Springer International Publishing}},
  title        = {{{A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets}}},
  doi          = {{10.1007/978-3-319-07494-8_18}},
  volume       = {{288}},
  year         = {{2014}},
}