---
res:
  bibo_abstract:
  - Multi-objective evolutionary algorithms (MOEAs) are generally designed to find
    a well spread Pareto-front approximation. Often, only a small section of this
    front may be of practical interest. Desirability functions (DFs) are able to describe
    user preferences intuitively. Furthermore, DFs can be attached to any fitness
    function easily. This way, desirability functions can help in guiding MOEAs without
    introducing additional restrictions or changes to the algorithm. The application
    of noisy fitness functions is not straight forward but relevant to many real-world
    problems. Therefore, a variant of Harrington’s one-sided desirability function
    using expectations is introduced which takes noise into account. A deterministic
    strategy as well as the XSGA-II are used in combination with DF to solve a noisy
    Binh problem and a noisy cost estimation problem for turning processes.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Jorn
      foaf_name: Mehnen, Jorn
      foaf_surname: Mehnen
  - 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: Ashutosh
      foaf_name: Tiwari, Ashutosh
      foaf_surname: Tiwari
  bibo_doi: 10.1109/CEC.2007.4424810
  dct_date: 2007^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/1941-0026
  dct_language: eng
  dct_title: Introducing user preference using Desirability Functions in Multi-Objective
    Evolutionary Optimisation of noisy processes@
...