---
res:
  bibo_abstract:
  - A systematic approach for determining the generation number at which a specific
    Multi-Objective Evolutionary Algorithm (MOEA) has converged for a given optimization
    problem is introduced. Convergence is measured by the performance indicators Generational
    Distance, Spread and Hypervolume. The stochastic nature of the MOEA is taken into
    account by repeated runs per generation number which results in a highly robust
    procedure. For each generation number the MOEA is repeated a fixed number of times,
    and the Kolmogorow-Smirnov-Test is used in order to decide if a significant change
    in performance is gained in comparison to preceding generations. A comparison
    of different MOEAs on a problem with respect to necessary generation numbers becomes
    possible, and the understanding of the algorithm’s behaviour is supported by analysing
    the development of the indicator values. The procedure is illustrated by means
    of standard test problems.@eng
  bibo_authorlist:
  - 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: Uwe
      foaf_name: Ligges, Uwe
      foaf_surname: Ligges
  - foaf_Person:
      foaf_givenName: Jörn
      foaf_name: Mehnen, Jörn
      foaf_surname: Mehnen
  - foaf_Person:
      foaf_givenName: Mike
      foaf_name: Preuss, Mike
      foaf_surname: Preuss
  dct_date: 2008^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/978-3-540-87700-4
  dct_language: eng
  dct_publisher: Springer Berlin Heidelberg@
  dct_title: A Convergence Criterion for Multiobjective Evolutionary Algorithms Based
    on Systematic Statistical Testing@
...