@inproceedings{46397,
  abstract     = {{In multiobjective optimization, set-based performance indicators are commonly used to assess the quality of a Pareto front approximation. Based on the scalarization obtained by these indicators, a performance comparison of multiobjective optimization algorithms becomes possible. The R2 and the Hypervolume (HV) indicator represent two recommended approaches which have shown a correlated behavior in recent empirical studies. Whereas the HV indicator has been comprehensively analyzed in the last years, almost no studies on the R2 indicator exist. In this paper, we thus perform a comprehensive investigation of the properties of the R2 indicator in a theoretical and empirical way. The influence of the number and distribution of the weight vectors on the optimal distribution of μ solutions is analyzed. Based on a comparative analysis, specific characteristics and differences of the R2 and HV indicator are presented.}},
  author       = {{Brockhoff, Dimo and Wagner, Tobias and Trautmann, Heike}},
  booktitle    = {{Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation}},
  isbn         = {{9781450311779}},
  keywords     = {{hypervolume indicator, multiobjective optimization, performance assessment, r2 indicator}},
  pages        = {{465–472}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{On the Properties of the R2 Indicator}}},
  doi          = {{10.1145/2330163.2330230}},
  year         = {{2012}},
}

@inproceedings{46396,
  abstract     = {{The steady supply of new optimization methods makes the algorithm selection problem (ASP) an increasingly pressing and challenging task, specially for real-world black-box optimization problems. The introduced approach considers the ASP as a cost-sensitive classification task which is based on Exploratory Landscape Analysis. Low-level features gathered by systematic sampling of the function on the feasible set are used to predict a well-performing algorithm out of a given portfolio. Example-specific label costs are defined by the expected runtime of each candidate algorithm. We use one-sided support vector regression to solve this learning problem. The approach is illustrated by means of the optimization problems and algorithms of the BBOB’09/10 workshop.}},
  author       = {{Bischl, Bernd and Mersmann, Olaf and Trautmann, Heike and Preuß, Mike}},
  booktitle    = {{Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation}},
  isbn         = {{9781450311779}},
  keywords     = {{machine learning, exploratory landscape analysis, fitness landscape, benchmarking, evolutionary optimization, bbob test set, algorithm selection}},
  pages        = {{313–320}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning}}},
  doi          = {{10.1145/2330163.2330209}},
  year         = {{2012}},
}