Variable Preference Modeling Using Multi-Objective Evolutionary Algorithms

  • Christian Hirsch
  • Pradyumn Kumar Shukla
  • Hartmut Schmeck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6576)


Decision making in the presence of multiple and conflicting objectives requires preference from the decision maker. The decision maker’s preferences give rise to a domination structure. Till now, most of the research has been focussed on the standard domination structure based on the Pareto-domination principle. However, various real world applications like medical image registration, financial applications, multi-criteria n-person games, among others, or even the preference model of decision makers frequently give rise to a so-called variable domination structure, in which the domination itself changes from point to point. Although variable domination is studied in the classical community since the early seventies, we could not find a single study in the evolutionary domain, even though, as the results of this paper show, multi-objective evolutionary algorithms can deal with the vagaries of a variable domination structure. The contributions of this paper are multiple-folds. Firstly, the algorithms are shown to be able to find a well-diverse set of the optimal solutions satisfying a variable domination structure. This is shown by simulation results on a number of test-problems. Secondly, it answers a hitherto open question in the classical community to develop a numerical method for finding a well-diverse set of such solutions. Thirdly, theoretical results are derived which facilitate the use of an evolutionary multi-objective algorithm. The theoretical results are of importance on their own. The results of this paper adequately show the niche of multi-objective evolutionary algorithms in variable preference modeling.


Minimal Point Objective Space Nondominated Solution Nondominated Point Inverted Generational Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christian Hirsch
    • 1
  • Pradyumn Kumar Shukla
    • 1
  • Hartmut Schmeck
    • 1
  1. 1.Institute AIFBKarlsruhe Institute of TechnologyKarlsruheGermany

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