Intelligent Data Engineering and Automated Learning – IDEAL 2006

Volume 4224 of the series Lecture Notes in Computer Science pp 410-418

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

  • Ofer M. ShirAffiliated withNatural Computing Group, Leiden University
  • , Joost N. KokAffiliated withNatural Computing Group, Leiden University
  • , Thomas BäckAffiliated withNatural Computing Group, Leiden University
  • , Marc J. J. VrakkingAffiliated withInstitute for Atomic and Molecular Physics, Amolf-FOM

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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.