SPAM: Set Preference Algorithm for Multiobjective Optimization

  • Eckart Zitzler
  • Lothar Thiele
  • Johannes Bader
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


This paper pursues the idea of a general multiobjective optimizer that can be flexibly adapted to arbitrary user preferences—assuming that the goal is to approximate the Pareto-optimal set. It proposes the Set Preference Algorithm for Multiobjective Optimization (SPAM) the working principle of which is based on two observations: (i) current multiobjective evolutionary algorithms (MOEAs) can be regarded as hill climbers on set problems and (ii) specific user preferences are often (implicitly) expressed in terms of a binary relation on Pareto set approximations. SPAM realizes a (1 + 1)-strategy on the space of Pareto set approximations and can be used with any type of set preference relations, i.e., binary relations that define a total preorder on Pareto set approximations. The experimental results demonstrate for a range of set preference relations that SPAM provides full flexibility with respect to user preferences and is effective in optimizing according to the specified preferences. It thereby offers a new perspective on preference-guided multiobjective search.


Preference Relation Evolutionary Computation Multiobjective Optimization User Preference Hill Climber 
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 2008

Authors and Affiliations

  • Eckart Zitzler
    • 1
  • Lothar Thiele
    • 1
  • Johannes Bader
    • 1
  1. 1.Computer Engineering and Networks LaboratoryETH ZurichSwitzerland

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