Learning the Complete-Basis-Functions Parameterization for the Optimization of Dynamic Molecular Alignment by ES

  • Ofer M. Shir
  • Joost N. Kok
  • Thomas Bäck
  • Marc J. J. Vrakking
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


This study further investigates the complete-basis-functions parameterization method (CBFP) for Evolution Strategies (ES), and its application to a challenging real-life high-dimensional physics optimization problem, namely Femtosecond Laser Pulse Shaping.

The CBFP method, which was introduced recently for tackling efficiently the learning task of n-variables functions, is combined here, for the first time, with niching techniques, and shown to boost the learning process of the given laser problem, and to yield satisfying multiple optima.

Moreover, a technique for learning the basis-functions and improving this method is outlined.


Phase Function Search Point Molecular Alignment Evolution Strategy Covariance Matrix Adaptation Evolution Strategy 
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
  • Joost N. Kok
    • 1
  • Thomas Bäck
    • 1
  • Marc J. J. Vrakking
    • 2
  1. 1.Natural Computing GroupLeiden UniversityLeidenThe Netherlands
  2. 2.Institute for Atomic and Molecular PhysicsAmolf-FOMAmsterdamThe Netherlands

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