Defining and Optimizing Indicator-Based Diversity Measures in Multiobjective Search

  • Tamara Ulrich
  • Johannes Bader
  • Lothar Thiele
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6238)


In this paper, we elaborate how decision space diversity can be integrated into indicator-based multiobjective search. We introduce DIOP, the diversity integrating multiobjective optimizer, which concurrently optimizes two set-based diversity measures, one in decision space and the other in objective space. We introduce a possibility to improve the diversity of a solution set, where the minimum proximity of these solutions to the Pareto-front is user-defined. Experiments show that DIOP is able to optimize both diversity measures and that the decision space diversity can indeed be improved if the required maximum distance of the solutions to the front is relaxed.


Diversity Measure Multiobjective Optimization Objective Space Quality Constraint Decision Space 
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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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tamara Ulrich
    • 1
  • Johannes Bader
    • 1
  • Lothar Thiele
    • 1
  1. 1.Computer Engineering and Networks LaboratoryETH ZurichZurichSwitzerland

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