@inproceedings{48867,
  abstract     = {{Assessing the performance of stochastic optimization algorithms in the field of multi-objective optimization is of utmost importance. Besides the visual comparison of the obtained approximation sets, more sophisticated methods have been proposed in the last decade, e. g., a variety of quantitative performance indicators or statistical tests. In this paper, we present tools implemented in the R package ecr, which assist in performing comprehensive and sound comparison and evaluation of multi-objective evolutionary algorithms following recommendations from the literature.}},
  author       = {{Bossek, Jakob}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  isbn         = {{978-1-4503-5764-7}},
  keywords     = {{evolutionary optimization, performance assessment, software-tools}},
  pages        = {{1350–1356}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package ecr}}},
  doi          = {{10.1145/3205651.3208312}},
  year         = {{2018}},
}

@inproceedings{48885,
  abstract     = {{Performance comparisons of optimization algorithms are heavily influenced by the underlying indicator(s). In this paper we investigate commonly used performance indicators for single-objective stochastic solvers, such as the Penalized Average Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a methodology for analyzing the effects of (usually heuristically set) indicator parametrizations - such as the penalty factor and the method used for aggregating across multiple runs - w.r.t. the robustness of the considered optimization algorithms.}},
  author       = {{Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  isbn         = {{978-1-4503-5764-7}},
  keywords     = {{algorithm selection, optimization, performance measures, transportation, travelling salesperson problem}},
  pages        = {{1737–1744}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers}}},
  doi          = {{10.1145/3205651.3208233}},
  year         = {{2018}},
}