Analyzing the Effect of Objective Correlation on the Efficient Set of MNK-Landscapes

  • Sébastien Verel
  • Arnaud Liefooghe
  • Laetitia Jourdan
  • Clarisse Dhaenens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6683)


In multiobjective combinatorial optimization, there exists two main classes of metaheuristics, based either on multiple aggregations, or on a dominance relation. As in the single-objective case, the structure of the search space can explain the difficulty for multiobjective metaheuristics, and guide the design of such methods. In this work we analyze the properties of multiobjective combinatorial search spaces. In particular, we focus on the features related the efficient set, and we pay a particular attention to the correlation between objectives. Few benchmark takes such objective correlation into account. Here, we define a general method to design multiobjective problems with correlation. As an example, we extend the well-known multiobjective NK-landscapes. By measuring different properties of the search space, we show the importance of considering the objective correlation on the design of metaheuristics.


Search Space Pareto Front Objective Space Multiobjective Problem Large Connected Component 
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

  • Sébastien Verel
    • 1
    • 3
  • Arnaud Liefooghe
    • 2
    • 3
  • Laetitia Jourdan
    • 3
  • Clarisse Dhaenens
    • 2
    • 3
  1. 1.CNRSUniversity of Nice Sophia AntipolisFrance
  2. 2.LIFL – CNRSUniversité Lille 1France
  3. 3.INRIA Lille-Nord EuropeFrance

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