---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Thomas
      foaf_name: Voß, Thomas
      foaf_surname: Voß
  - foaf_Person:
      foaf_givenName: Heike
      foaf_name: Trautmann, Heike
      foaf_surname: Trautmann
      foaf_workInfoHomepage: http://www.librecat.org/personId=100740
    orcid: 0000-0002-9788-8282
  - foaf_Person:
      foaf_givenName: Christian
      foaf_name: Igel, Christian
      foaf_surname: Igel
  bibo_doi: https://doi.org/10.1007/978-3-642-15871-1_27
  dct_date: 2010^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/978-3-642-15871-1
  dct_language: eng
  dct_publisher: Springer Berlin Heidelberg@
  dct_title: New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization@
...