Niching in Evolution Strategies and Its Application to Laser Pulse Shaping

  • Ofer M. Shir
  • Christian Siedschlag
  • Thomas Bäck
  • Marc J. J. Vrakking
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3871)


Evolutionary Algorithms (EAs), popular search methods for optimization problems, are known for successful and fast location of single optimal solutions. However, many complex search problems require the location and maintenance of multiple solutions. Niching methods, the extension of EAs to address this issue, have been investigated up to date mainly within the field of Genetic Algorithms (GAs), and their applications were limited to low-dimensional search problems.

In this paper we present in detail the background for niching methods within Evolution Strategies (ES), and discuss two ES niching methods, which have been introduced recently and have been tested only for theoretical functions. We describe the application of those ES niching methods to a challenging real-life high-dimensional optimization problem, namely Femtosecond Laser Pulse Shaping. The methods are shown to be robust and to achieve satisfying results for the given problem.


Evolution Strategy Search Point Random Genetic Drift Evolution Strategy Covariance Matrix Adaptation 
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 2006

Authors and Affiliations

  • Ofer M. Shir
    • 1
  • Christian Siedschlag
    • 2
  • Thomas Bäck
    • 1
    • 3
  • Marc J. J. Vrakking
    • 2
  1. 1.Leiden Institute of Advanced Computer ScienceUniversiteit LeidenLeidenThe Netherlands
  2. 2.FOM-Instituut AMOLFAmsterdamThe Netherlands
  3. 3.NuTech SolutionsDortmundGermany

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