Multi-Noisy-objective Optimization Based on Prediction of Worst-Case Performance

  • Kiyoharu Tagawa
  • Shoichi Harada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8890)


This paper proposes a new approach to cope with multi-objective optimization problems in presence of noise. In the first place, since considering the worst-case performance is important in many real-world optimization problems, a solution is evaluated based on the upper bounds of respective noisy objective functions predicted statistically by multiple sampling. Secondary, a rational way to decide the maximum sample size for the solution is shown. Thirdly, to allocate the computing budget of a proposed evolutionary algorithm only to promising solutions, two pruning techniques are contrived to judge hopeless solutions only by a few sampling and skip the evaluation of the upper bounds for them.


evolutionary computing multi-objective optimization 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kiyoharu Tagawa
    • 1
  • Shoichi Harada
    • 2
  1. 1.School of Science and EngineeringKinki UniversityHigashi-OsakaJapan
  2. 2.Graduate School of Science and Engineering ResearchKinki UniversityHigashi-OsakaJapan

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