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A decoder-based evolutionary algorithm for constrained parameter optimization problems

  • Slawomir Koziel
  • Zbigniew Michalewicz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)

Abstract

Several methods have been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; a survey paper [7] provides an overview of various techniques and some experimental results, as well as proposes a set of eleven test problems. Recently a new, decoder-based approach for solving constrained numerical optimization problems was proposed [2, 3]. The proposed method defines a homomorphous mapping between n-dimensional cube and a feasible search space. In [3] we have demonstrated the power of this new approach on several test cases. However, it is possible to enhance the performance of the system even further by introducing additional concepts of (1) nonlinear mappings with an adaptive parameter, and (2) adaptive location of the reference point of the mapping.

Keywords

Reference Point Search Space Line Segment Search Point Infeasible Solution 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Slawomir Koziel
    • 1
  • Zbigniew Michalewicz
    • 2
  1. 1.Department of Electronics, Telecommunication and InformaticsTechnical University of GdańskGdańskPoland
  2. 2.Department of Computer ScienceUniversity of North CarolinaCharlotteUSA

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