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Metamodel—Assisted Evolution Strategies

  • Michael Emmerich
  • Alexios Giotis
  • Mutlu Özdemir
  • Thomas Bäck
  • Kyriakos Giannakoglou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

This paper presents various Metamodel–Assisted Evolution Strategies which reduce the computational cost of optimisation problems involving time—consuming function evaluations. The metamodel is built using previously evaluated solutions in the search space and utilized to predict the fitness of new candidate solutions. In addition to previous works by the authors, the new metamodel takes also into account the error associated with each prediction, by correlating neighboring points in the search space. A mathematical problem and the problem of designing an optimal airfoil shape under viscous flow considerations have been worked out. Both demonstrate the noticeable gain in computational time one might expect from the use of metamodels in Evolution Strategies.

Keywords

Search Space Exact Evaluation Evolution Strategy Local Error Estimation Optimal Aerodynamic Shape 
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 2002

Authors and Affiliations

  • Michael Emmerich
    • 1
  • Alexios Giotis
    • 3
  • Mutlu Özdemir
    • 1
  • Thomas Bäck
    • 2
  • Kyriakos Giannakoglou
    • 3
  1. 1.Center for Applied Systems AnalysisInformatik Centrum DortmundDortmundGermany
  2. 2.NuTech Solutions GmbHDortmundGermany
  3. 3.Dept. of Mechanical EngineeringNational Technical University of AthensAthensGreece

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