@inproceedings{46409,
  abstract     = {{Since many real-world optimization problems are noisy, vector optimization algorithms that can cope with noise and uncertainty are required. We propose new, robust selection strategies for evolutionary multi-objective optimization in the presence of noise. We apply new measures of uncertainty for estimating the recently introduced Pareto-dominance for uncertain and noisy environments (PDU). The first measure is the inter-quartile range of the outcomes of repeated function evaluations. The second is based on axis-aligned bounding boxes around the upper and lower quantiles of the sampled fitness values in objective space. Experiments on real and artificial problems show promising results.}},
  author       = {{Voß, Thomas and Trautmann, Heike and Igel, Christian}},
  booktitle    = {{Parallel Problem Solving from Nature, PPSN XI}},
  editor       = {{Schaefer, Robert and Cotta, Carlos and Kołodziej, Joanna and Rudolph, Günter}},
  isbn         = {{978-3-642-15871-1}},
  pages        = {{260–269}},
  publisher    = {{Springer Berlin Heidelberg}},
  title        = {{{New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization}}},
  doi          = {{https://doi.org/10.1007/978-3-642-15871-1_27}},
  year         = {{2010}},
}