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On the Benefits of Random Memorizing in Local Evolutionary Search

  • Hans-Michael Voigt
  • Jan Matti Lange
Conference paper
  • 571 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1424)

Abstract

For the calibration of laser induced plasma spectrometers robust and efficient local search methods are required. Therefore, several local optimizers from nonlinear optimization, random search and evolutionary computation are compared. It is shown that evolutionary algorithms are superior with respect to reliability and efficiency. To enhance the local search of an evolutionary algorithm a new method of random memorizing is introduced. It leads to a substantial gain in efficiency for a reliable local search.

Keywords

Local Search Evolution Strategy Covariance Matrix Adaptation Sequential Memory Calibration Model Parameter 
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 1998

Authors and Affiliations

  • Hans-Michael Voigt
    • 1
  • Jan Matti Lange
    • 1
  1. 1.GFaI — Center for Applied Computer ScienceBerlinGermany

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