Robust Optimization by ε-Ranking on High Dimensional Objective Spaces

  • Hernán Aguirre
  • Kiyoshi Tanaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)


This work proposes a method to fine grain the ranking of solutions after they have been ranked by Pareto dominance, aiming to improve the performance of evolutionary algorithms on many objectives optimization problems. The re-ranking method uses a randomized sampling procedure to choose, from sets of equally ranked solutions, those solutions that will be given selective advantage. The sampling procedure favors a good distribution of the sampled solutions based on dominance regions wider than conventional Pareto dominance. We enhance NSGA-II with the proposed method and test its performance on MNK-Landscapes with up to M = 10 objectives. Experimental results show that convergence and diversity of the solutions found can improve remarkably on 3 ≤ M ≤ 10 objectives problems.


Multiobjective Optimization Pareto Optimal Solution Epistatic Interaction Objective Space Robust Optimization 
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

  • Hernán Aguirre
    • 1
    • 2
  • Kiyoshi Tanaka
    • 2
  1. 1.Fiber-Nanotech Young Researcher Empowerment ProgramJapan
  2. 2.Faculty of EngineeringShinshu UniversityNaganoJapan

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