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Accelerating the evolutionary-gradient-search procedure: Individual step sizes

  • Ralf Salomon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)

Abstract

Recent research has proposed the evolutionary-gradient-search procedure that uses the evolutionary scheme to estimate a gradient direction and that performs the parameter updates in a steepest-descent form. On several test functions, the procedure has shown faster convergence than other evolutionary algorithms. However, the procedure also exhibits similar deficiencies as steepest-descent methods. This paper explores to which extent the adoption of individual step sizes, as known from evolution strategies, can be beneficially used. It turns out that they considerably accelerate convergence.

Keywords

Evolutionary Algorithm Evolution Strategy Correlate Mutation Acceleration Method Mutation Vector 
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

  • Ralf Salomon
    • 1
  1. 1.AI Lab, Department of Computer ScienceUniversity of ZurichZurichSwitzerland

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